Interface Singularity

Every era of technology hides a silent turning point, an unseen moment when the tools we use begin to use us. For centuries, interfaces were the boundary between human intent and machine execution, a keyboard, a touchscreen, a button. But today, that boundary is dissolving. We are entering what I call the Interface Singularity, a convergence point where artificial intelligence no longer sits behind the interface, but becomes it. In this paradigm, AI agents won’t just assist us, they act for us. They navigate, decide, create, and transact on our behalf. The very concept of “using“ a platform is being abstracted away. You no longer open your banking app, you ask your AI to handle your finances. You don’t manually write a blog, you instruct your model to draft, refine, and publish it. You don’t search the web, you converse with an intelligence that already knows where to look. What began as a mere convenience is evolving into delegation of identity itself. delegation of identity itself delegation of identity itself This shift didn’t happen overnight. It emerged in stages, subtle, almost invisible progression that seemed unrelated. An AI assistant here, a smart checkout protocol there, an AI-native browser in development elsewhere. People see these as isolated breakthroughs. But I don’t. My analysis suggests that these are fragments of a single trajectory, one that leads inevitably toward interface singularity. Each of these innovations, AI copilots, agentic protocols, identity frameworks, AI-powered browsers, are pieces of the same puzzle. They are steps toward an ecosystem where the interface disappears, replaced by agents that represent, mediate, and eventually embody us in digital systems. embody In the pages that follow, I’ll map this trajectory in three stages, the abstraction of control, the abstraction of creation, the abstraction of interaction, and the convergence of identity and interface, to show that what seems like innovation in isolation is in fact the architecture of a new era in human–machine relations. the abstraction of control the abstraction of creation, the abstraction of interaction, and the convergence of identity and interface Abstraction of Control Before computers spoke our language, humans had to speak the machine’s, and that was the first stage of interface. The earliest “interfaces” were not screens, not even text commands, they were physical, electrical, and unforgiving. To make a machine do anything, a developer had to understand its circuits, voltages, and timing signals. In other words, you didn’t just use a computer, you engineered it every time you wanted to compute. the machine’s, and that was the first stage of interface first stage of interface interfaces In the 1940s and 1950s, programs were wired logic by hand. Machines like the ENIAC (Electronic Numerical Integrator and Computer) were programmed by plugging cables into panels and setting hundreds of switches manually. This wasn’t metaphorical “circuit thinking”; it was literal: developers were acting as human interfaces, translating human logic into electrical pathways. Machines like the ENIAC (Electronic Numerical Integrator and Computer) Early computers such as the UNIVAC or Colossus had no separation between hardware and programming. Developers were hardware technicians, mathematicians, and inventors rolled into one. Their “UI” was a control panel with blinking lights, each bulb a fragment of a binary message. To perform a single task, for example, calculating artillery trajectories, one had to rewire sections of the machine, set memory states by hand, and manage electrical noise. Debugging often meant walking around the room with a voltmeter. This was machine-level interaction, raw, direct, and precise. Every calculation was a physical voodoo. machine-level interaction raw direct precise physical voodoo The next leap came when humans stopped flipping switches and started writing symbols. Assembly language was the first attempt to abstract raw machine code into something legible. Instead of writing `Hello, World!` which is the first universal line of code any developer from any language writes, this is what has been abstracted away. 01001000 01100101 01101100 01101100 01101111 00101100 00100000 01110111 01101111 01110010 01101100 01100100 00100001 01001000 01100101 01101100 01101100 01101111 00101100 00100000 01110111 01101111 01110010 01101100 01100100 00100001 It was still deeply technical, every instructions still mapped one-to-one to machine behavior, but for the first time, developers could think in operations rather than voltages. And this was the critical shift in the interface paradigm, the computer started to speak a symbolic dialect of human intent, which developers could reason about logic, loops, and data without needing to think about resistors or capacitors. interface paradigm logic loops data resistors capacitors Yet, this interface was still reserved for experts. You needed years of specialized training to write meaningful code. The machine was no longer physical, but was still alien. By the late 1950s and early 1960s, new high-level languages such as FORTRAN, COBOL, and LISP pushed the abstraction boundary further, allowing developers to describe what to compute rather than how to compute. FORTRAN, designed for scientists, and COBOL, designed for business, translated human-readable syntax into machine instructions automatically, enabling programmers to focus on algorithms and problem-solving instead of hardware. A simple loop like below. FORTRAN COBOL LISP what how DO 10 I = 1, 10 PRINT *, I 10 CONTINUE DO 10 I = 1, 10 PRINT *, I 10 CONTINUE Once a series of low-level jumps and register moves, could now be written and understood by someone with only mathematical training, marking the birth of conceptual programming where the interface became linguistic rather than mechanical. conceptual programming linguistic mechanical This evolution continued as punch-card programming gave way to keyboards and time-sharing terminals in the 1970s, creating interactive dialogue between human and machine through the command line interface, which, though terse and symbolic, replaced mechanical batching with immediate feedback. punch-card punch-card keyboards time-sharing terminals 1970s human machine command line interface command line interface terse symbolic mechanical batching immediate feedback The 1980s introduced the graphical user interface, pioneered by Xerox PARC and popularized by Apple’s Lisa and Macintosh as well as Microsoft Windows, which replaced textual syntax with spatial metaphors, like users could manipulate icons, windows, and menus instead of memorizing commands, marking the first mainstream abstraction of control and transforming the computer into an interactive environment. 1980s graphical user interface graphical user interface Xerox PARC Xerox PARC Apple’s Lisa Apple’s Lisa Macintosh Macintosh Microsoft Windows Microsoft Windows textual syntax metaphors icons windows menus memorizing commands abstraction of control interactive environment Also in the 1990s, the web and browsers, abstracting not only the machine but the network, hiding the complexity of protocols, servers, and databases behind hyperlinks and forms, democratizing information, creation, and commerce. web browsers abstracting machine network complexity of protocols servers databases hyperlinks forms democratizing information creation commerce Mobile devices, touchscreen gestures, and later voice assistants like Siri, Alexa, and Google Assistant further reduced friction by removing the need for cursors or typing, though interfaces remained procedural, users still issued commands rather than expressing goals. Mobile devices touchscreen gestures voice assistants Siri Siri Alexa Alexa Google Assistant Google Assistant removing cursors typing interfaces procedural procedural issued commands expressing goals As each layer of interface abstraction removed the need for direct control, moving from wiring circuits to writing symbolic instructions, from punching cards to typing at a terminal, and from command lines to graphical icons and web pages, humans gradually shifted from manually creating every detail to orchestrating outcomes. interface abstraction direct control wiring circuits writing symbolic instructions punching cards typing at a terminal command lines graphical icons web pages manually creating manually creating orchestrating outcomes orchestrating outcomes Which this distinction set the stage for AI as an interface, inaugurating the era of the Abstraction of Creation, where human intent, rather than technical instruction, directly drives outcomes. With AI models translating ideas into tangible creation, the keyboard remains, but its meaning has changed, users no longer program the computer, they collaborate with it, making the interface itself an interpreter of human intent. inaugurating Abstraction of Creation intent technical instruction directly drives outcomes AI models ideas tangible creation keyboard remains no longer program the computer collaborate with it interface itself interpreter of human intent Evolution of interfaces, including era, interface type, examples/metaphors, and the level of abstraction: Era Interface Type Examples / Metaphors Abstraction Level 1940s–1950s Machine / Hardware Control Wiring panels, switches, blinking lights Very low: Direct manipulation of circuits; human acts as the interface 1950s–1960s Assembly / Symbolic Language MOV AL, 61h, simple loops Low: Symbolic representation of machine instructions; still requires technical expertise Late 1950s–1960s High-Level Languages FORTRAN, COBOL, LISP Medium: Describe what to compute, not how; conceptual programming begins 1960s–1970s Terminals / Keyboards Punch cards → keyboards, time-sharing terminals Medium: Interactive dialogue with machine; immediate feedback replaces batch processing 1980s Graphical User Interface (GUI) Windows, icons, folders, trash bin, drag-and-drop High: Spatial metaphors replace textual syntax; users interact with visual objects 1990s Web Browser / Universal Interface HTML pages, hyperlinks, forms Very high: Abstracts machine and network complexity; democratizes creation and access 2000s–2010s Touch / Gesture / Voice Interfaces Touchscreens, swipes, taps, Siri, Alexa Very high: Procedural friction reduced; still command-driven but more natural inputs 2022s+ AI-Driven Interfaces Prompting AI to create webpages, images, or code Extreme: Human intent directly translated into creation; interface acts as collaborator Era Interface Type Examples / Metaphors Abstraction Level 1940s–1950s Machine / Hardware Control Wiring panels, switches, blinking lights Very low: Direct manipulation of circuits; human acts as the interface 1950s–1960s Assembly / Symbolic Language MOV AL, 61h, simple loops Low: Symbolic representation of machine instructions; still requires technical expertise Late 1950s–1960s High-Level Languages FORTRAN, COBOL, LISP Medium: Describe what to compute, not how; conceptual programming begins 1960s–1970s Terminals / Keyboards Punch cards → keyboards, time-sharing terminals Medium: Interactive dialogue with machine; immediate feedback replaces batch processing 1980s Graphical User Interface (GUI) Windows, icons, folders, trash bin, drag-and-drop High: Spatial metaphors replace textual syntax; users interact with visual objects 1990s Web Browser / Universal Interface HTML pages, hyperlinks, forms Very high: Abstracts machine and network complexity; democratizes creation and access 2000s–2010s Touch / Gesture / Voice Interfaces Touchscreens, swipes, taps, Siri, Alexa Very high: Procedural friction reduced; still command-driven but more natural inputs 2022s+ AI-Driven Interfaces Prompting AI to create webpages, images, or code Extreme: Human intent directly translated into creation; interface acts as collaborator Era Interface Type Examples / Metaphors Abstraction Level Era Era Interface Type Interface Type Examples / Metaphors Examples / Metaphors Abstraction Level Abstraction Level 1940s–1950s Machine / Hardware Control Wiring panels, switches, blinking lights Very low: Direct manipulation of circuits; human acts as the interface 1940s–1950s 1940s–1950s Machine / Hardware Control Machine / Hardware Control Wiring panels, switches, blinking lights Wiring panels, switches, blinking lights Wiring panels switches blinking lights Very low: Direct manipulation of circuits; human acts as the interface Very low: Direct manipulation of circuits; human acts as the interface Very low 1950s–1960s Assembly / Symbolic Language MOV AL, 61h, simple loops Low: Symbolic representation of machine instructions; still requires technical expertise 1950s–1960s 1950s–1960s Assembly / Symbolic Language Assembly / Symbolic Language MOV AL, 61h, simple loops MOV AL, 61h, simple loops MOV AL, 61h Low: Symbolic representation of machine instructions; still requires technical expertise Low: Symbolic representation of machine instructions; still requires technical expertise Low Late 1950s–1960s High-Level Languages FORTRAN, COBOL, LISP Medium: Describe what to compute, not how; conceptual programming begins Late 1950s–1960s Late 1950s–1960s High-Level Languages High-Level Languages FORTRAN, COBOL, LISP FORTRAN, COBOL, LISP FORTRAN COBOL LISP Medium: Describe what to compute, not how; conceptual programming begins Medium: Describe what to compute, not how; conceptual programming begins Medium what how 1960s–1970s Terminals / Keyboards Punch cards → keyboards, time-sharing terminals Medium: Interactive dialogue with machine; immediate feedback replaces batch processing 1960s–1970s 1960s–1970s Terminals / Keyboards Terminals / Keyboards Punch cards → keyboards, time-sharing terminals Punch cards → keyboards, time-sharing terminals Punch cards keyboards time-sharing terminals Medium: Interactive dialogue with machine; immediate feedback replaces batch processing Medium: Interactive dialogue with machine; immediate feedback replaces batch processing Medium 1980s Graphical User Interface (GUI) Windows, icons, folders, trash bin, drag-and-drop High: Spatial metaphors replace textual syntax; users interact with visual objects 1980s 1980s Graphical User Interface (GUI) Graphical User Interface (GUI) Windows, icons, folders, trash bin, drag-and-drop Windows, icons, folders, trash bin, drag-and-drop High: Spatial metaphors replace textual syntax; users interact with visual objects High: Spatial metaphors replace textual syntax; users interact with visual objects High 1990s Web Browser / Universal Interface HTML pages, hyperlinks, forms Very high: Abstracts machine and network complexity; democratizes creation and access 1990s 1990s Web Browser / Universal Interface Web Browser / Universal Interface HTML pages, hyperlinks, forms HTML pages, hyperlinks, forms Very high: Abstracts machine and network complexity; democratizes creation and access Very high: Abstracts machine and network complexity; democratizes creation and access Very high 2000s–2010s Touch / Gesture / Voice Interfaces Touchscreens, swipes, taps, Siri, Alexa Very high: Procedural friction reduced; still command-driven but more natural inputs 2000s–2010s 2000s–2010s Touch / Gesture / Voice Interfaces Touch / Gesture / Voice Interfaces Touchscreens, swipes, taps, Siri, Alexa Touchscreens, swipes, taps, Siri, Alexa Very high: Procedural friction reduced; still command-driven but more natural inputs Very high: Procedural friction reduced; still command-driven but more natural inputs Very high 2022s+ AI-Driven Interfaces Prompting AI to create webpages, images, or code Extreme: Human intent directly translated into creation; interface acts as collaborator 2022s+ 2022s+ AI-Driven Interfaces AI-Driven Interfaces Prompting AI to create webpages, images, or code Prompting AI to create webpages, images, or code Extreme: Human intent directly translated into creation; interface acts as collaborator Extreme: Human intent directly translated into creation; interface acts as collaborator Extreme Abstraction of Creation From the first stage, Abstraction of Control, knowing every dial, every command, every rule of engagement between human and machine. To create, you first had to control, to set up systems, wire circuits, write instructions, and configure environments. But as control itself became abstracted, as interfaces began to manage complexity on our behalf, creation started to detach from the mechanics of making. Abstraction of Control every dial every command every rule engagement human machine to set up systems wire circuits write instructions configure environments interfaces Human Abstraction Abstraction, at its core, hides complexity behind a simpler interface, collapsing multi-step, domain-specific workflows into single expressions of intent. In software, this means spinning up a secure web server with a single command instead of configuring ports, firewalls, and certificates by hand. Even using a boilerplate or framework is an act of abstraction, someone has already handled the difficult parts, authentication, routing, email delivery, database migrations, state management, security hardening. Every package you install, from React to Express, from Tailwind to Stripe’s SDK, is a fragment of another creator’s expertise. These are human abstractions of creation, built by developers who spent years, sometimes decades, turning their hard-won knowledge into reusable interfaces. Abstraction hides complexity simpler interface collapsing multi-step domain-specific workflows single expressions intent spinning up a secure web server single command configuring ports firewalls certificates hand abstraction authentication routing email delivery database migrations state management security hardening React Express Tailwind Stripe’s SDK human abstractions of creation human abstractions of creation developers spent years sometimes decades hard-won knowledge reusable interfaces AI-built abstractions But what’s has already emerged now is different. The abstractions themselves are no longer handcrafted, they’re generated. AI doesn’t just package human expertise, it fabricates new creative shortcuts from the patterns of everything ever built. Instead of calling a library, you describe an outcome. Instead of composing functions, you compose intentions. That distinction marks a new threshold, a world where creation no longer relies on knowing how to build, only what to imagine. abstractions themselves handcrafted generated package human expertise fabricates new creative patterns of everything ever built how what That sounds simple, but the implications are enormous. Creation used to require domain knowledge (coding, UX, cinematography, music theory). AI flattens that expertise curve, prompts + a model = finished or nearly finished output. This is the foundational shift that makes the later stages possible. prompts model finished nearly finished output So instead of learning React, CSS, or database schema design, you simply describe what you want: React CSS database schema design “Build me a responsive dashboard with authentication and a payment system.” “Build me a responsive dashboard with authentication and a payment system.” And the model does it, configuring dependencies, writing code, setting up deployment pipelines, connecting databases, and even styling the UI. Stage One Materialization Below are examples of how Stage two (Abstraction of Creation) has already materialized and completed across almost all industries (law, medicine, finance, education, media, retail, travel, manufacturing, agriculture, real estate, construction, energy, telecom, logistics, gaming, biotech/pharma, government, security/cybersecurity, HR, and more). Domain What Stage 1 Looks Like Representative Tools / Links Software & Developer Tools Generate working code, scaffolding, tests, bug fixes, infra scripts, and even entire apps from prompts. GitHub Copilot • Copilot CLI & Agent • Replit Ghostwriter • Amazon CodeWhisperer • TabNine Writing & Knowledge Work Blog posts, product copy, PRs, internal docs, meeting minutes, legal summaries, and email drafts from prompts and templates. ChatGPT • Jasper • Copy.ai • Writesonic • Rytr Design & Visual Art Text → image, prompt-based layout, concept art, rapid A/B visual testing. DALL·E 3 • Midjourney • Stable Diffusion • Adobe Firefly Audio & Video Production Text → voice, script → video, automatic editing, localization. Synthesia • Descript • Runway • ElevenLabs Legal / Contracting Contract drafting, clause generation, legal summaries, and due diligence drafts. CoCounsel (Thomson Reuters) • Harvey AI • Casetext CoCounsel • Spellbook • DoNotPay Healthcare & Clinical Symptom assessment bots, clinical note generation, research summaries. Ada Health • Babylon Health • Buoy Health • K Health • Suki • Augmedix Finance & Accounting Automated reports, analysis drafts, anomaly detection, bookkeeping. MindBridge • BlackLine • Trullion • DataSnipper • Workiva Education & Learning Auto-summarization, curriculum generation, tutoring bots, auto-grading. Duolingo Max • Scribe • Gradescope • Coursera • edX Marketing & PR Ad copy, campaign concepts, audience segmentation, A/B creative generation. Jasper • Copy.ai • Persado • Phrasee Retail & E-commerce Product descriptions, localized images, automated A/B creatives, SKU metadata. Rephrase.ai • Jasper • Copy.ai • Shopify AI Tools Travel & Hospitality Itinerary drafts, travel guides, marketing brochures, localized copy. Zendesk AI Integrations • Various AI Itinerary Generators Manufacturing & Engineering Technical docs, maintenance procedures, CAD prompts, design drafts. Siemens Digital Industries • Autodesk Generative Design • NVIDIA Omniverse Agriculture & AgTech Drone imagery summaries, scouting reports, advisory templates. John Deere See & Spray • Climate FieldView Real Estate & PropTech Listing descriptions, market analysis summaries, tenant communication drafts. Zillow AI Tools • Various Broker CRM Generators Construction & Field Work Site reports, safety docs, progress summaries from images. OpenSpace • PlanGrid Energy & Utilities Incident reports, compliance drafts, sensor summary generation. Schneider Electric • Siemens Analytics Telecom & Contact Centers Transcripts, summaries, agent scripts, response templates. Interface.ai • Genesys • NICE • Five9 Logistics & Supply Chain Route summaries, freight docs, scheduling drafts, exception summaries. Convoy • FourKites Gaming & Entertainment Procedural dialogue, level concepts, NPC behavior templates. Unity ML-Agents • NVIDIA Omniverse Biotech & Pharma Research Literature summarization, protocol drafts, molecule ideation. Benchling • DeepMind AlphaFold Cybersecurity Draft incident summaries, playbooks, triage writeups. CrowdStrike • Palo Alto Cortex XDR Human Resources & Recruiting Job description generation, outreach drafts, onboarding checklists. HireVue • Lever • Greenhouse Fashion & Creative Crafts Pattern prompts, mock-ups, trend forecasts, lookbook generation. Adobe Firefly Domain What Stage 1 Looks Like Representative Tools / Links Software & Developer Tools Generate working code, scaffolding, tests, bug fixes, infra scripts, and even entire apps from prompts. GitHub Copilot • Copilot CLI & Agent • Replit Ghostwriter • Amazon CodeWhisperer • TabNine Writing & Knowledge Work Blog posts, product copy, PRs, internal docs, meeting minutes, legal summaries, and email drafts from prompts and templates. ChatGPT • Jasper • Copy.ai • Writesonic • Rytr Design & Visual Art Text → image, prompt-based layout, concept art, rapid A/B visual testing. DALL·E 3 • Midjourney • Stable Diffusion • Adobe Firefly Audio & Video Production Text → voice, script → video, automatic editing, localization. Synthesia • Descript • Runway • ElevenLabs Legal / Contracting Contract drafting, clause generation, legal summaries, and due diligence drafts. CoCounsel (Thomson Reuters) • Harvey AI • Casetext CoCounsel • Spellbook • DoNotPay Healthcare & Clinical Symptom assessment bots, clinical note generation, research summaries. Ada Health • Babylon Health • Buoy Health • K Health • Suki • Augmedix Finance & Accounting Automated reports, analysis drafts, anomaly detection, bookkeeping. MindBridge • BlackLine • Trullion • DataSnipper • Workiva Education & Learning Auto-summarization, curriculum generation, tutoring bots, auto-grading. Duolingo Max • Scribe • Gradescope • Coursera • edX Marketing & PR Ad copy, campaign concepts, audience segmentation, A/B creative generation. Jasper • Copy.ai • Persado • Phrasee Retail & E-commerce Product descriptions, localized images, automated A/B creatives, SKU metadata. Rephrase.ai • Jasper • Copy.ai • Shopify AI Tools Travel & Hospitality Itinerary drafts, travel guides, marketing brochures, localized copy. Zendesk AI Integrations • Various AI Itinerary Generators Manufacturing & Engineering Technical docs, maintenance procedures, CAD prompts, design drafts. Siemens Digital Industries • Autodesk Generative Design • NVIDIA Omniverse Agriculture & AgTech Drone imagery summaries, scouting reports, advisory templates. John Deere See & Spray • Climate FieldView Real Estate & PropTech Listing descriptions, market analysis summaries, tenant communication drafts. Zillow AI Tools • Various Broker CRM Generators Construction & Field Work Site reports, safety docs, progress summaries from images. OpenSpace • PlanGrid Energy & Utilities Incident reports, compliance drafts, sensor summary generation. Schneider Electric • Siemens Analytics Telecom & Contact Centers Transcripts, summaries, agent scripts, response templates. Interface.ai • Genesys • NICE • Five9 Logistics & Supply Chain Route summaries, freight docs, scheduling drafts, exception summaries. Convoy • FourKites Gaming & Entertainment Procedural dialogue, level concepts, NPC behavior templates. Unity ML-Agents • NVIDIA Omniverse Biotech & Pharma Research Literature summarization, protocol drafts, molecule ideation. Benchling • DeepMind AlphaFold Cybersecurity Draft incident summaries, playbooks, triage writeups. CrowdStrike • Palo Alto Cortex XDR Human Resources & Recruiting Job description generation, outreach drafts, onboarding checklists. HireVue • Lever • Greenhouse Fashion & Creative Crafts Pattern prompts, mock-ups, trend forecasts, lookbook generation. Adobe Firefly Domain What Stage 1 Looks Like Representative Tools / Links Domain Domain Domain What Stage 1 Looks Like What Stage 1 Looks Like What Stage 1 Looks Like Representative Tools / Links Representative Tools / Links Representative Tools / Links Software & Developer Tools Generate working code, scaffolding, tests, bug fixes, infra scripts, and even entire apps from prompts. GitHub Copilot • Copilot CLI & Agent • Replit Ghostwriter • Amazon CodeWhisperer • TabNine Software & Developer Tools Software & Developer Tools Software & Developer Tools Generate working code, scaffolding, tests, bug fixes, infra scripts, and even entire apps from prompts. Generate working code, scaffolding, tests, bug fixes, infra scripts, and even entire apps from prompts. GitHub Copilot • Copilot CLI & Agent • Replit Ghostwriter • Amazon CodeWhisperer • TabNine GitHub Copilot • Copilot CLI & Agent • Replit Ghostwriter • Amazon CodeWhisperer • TabNine GitHub Copilot Copilot CLI & Agent Replit Ghostwriter Amazon CodeWhisperer TabNine Writing & Knowledge Work Blog posts, product copy, PRs, internal docs, meeting minutes, legal summaries, and email drafts from prompts and templates. ChatGPT • Jasper • Copy.ai • Writesonic • Rytr Writing & Knowledge Work Writing & Knowledge Work Writing & Knowledge Work Blog posts, product copy, PRs, internal docs, meeting minutes, legal summaries, and email drafts from prompts and templates. Blog posts, product copy, PRs, internal docs, meeting minutes, legal summaries, and email drafts from prompts and templates. ChatGPT • Jasper • Copy.ai • Writesonic • Rytr ChatGPT • Jasper • Copy.ai • Writesonic • Rytr ChatGPT Jasper Copy.ai Writesonic Rytr Design & Visual Art Text → image, prompt-based layout, concept art, rapid A/B visual testing. DALL·E 3 • Midjourney • Stable Diffusion • Adobe Firefly Design & Visual Art Design & Visual Art Design & Visual Art Text → image, prompt-based layout, concept art, rapid A/B visual testing. Text → image, prompt-based layout, concept art, rapid A/B visual testing. DALL·E 3 • Midjourney • Stable Diffusion • Adobe Firefly DALL·E 3 • Midjourney • Stable Diffusion • Adobe Firefly DALL·E 3 Midjourney Stable Diffusion Adobe Firefly Audio & Video Production Text → voice, script → video, automatic editing, localization. Synthesia • Descript • Runway • ElevenLabs Audio & Video Production Audio & Video Production Audio & Video Production Text → voice, script → video, automatic editing, localization. Text → voice, script → video, automatic editing, localization. Synthesia • Descript • Runway • ElevenLabs Synthesia • Descript • Runway • ElevenLabs Synthesia Descript Runway ElevenLabs Legal / Contracting Contract drafting, clause generation, legal summaries, and due diligence drafts. CoCounsel (Thomson Reuters) • Harvey AI • Casetext CoCounsel • Spellbook • DoNotPay Legal / Contracting Legal / Contracting Legal / Contracting Contract drafting, clause generation, legal summaries, and due diligence drafts. Contract drafting, clause generation, legal summaries, and due diligence drafts. CoCounsel (Thomson Reuters) • Harvey AI • Casetext CoCounsel • Spellbook • DoNotPay CoCounsel (Thomson Reuters) • Harvey AI • Casetext CoCounsel • Spellbook • DoNotPay CoCounsel (Thomson Reuters) Harvey AI Casetext CoCounsel Spellbook DoNotPay Healthcare & Clinical Symptom assessment bots, clinical note generation, research summaries. Ada Health • Babylon Health • Buoy Health • K Health • Suki • Augmedix Healthcare & Clinical Healthcare & Clinical Healthcare & Clinical Symptom assessment bots, clinical note generation, research summaries. Symptom assessment bots, clinical note generation, research summaries. Ada Health • Babylon Health • Buoy Health • K Health • Suki • Augmedix Ada Health • Babylon Health • Buoy Health • K Health • Suki • Augmedix Ada Health Babylon Health Buoy Health K Health Suki Augmedix Finance & Accounting Automated reports, analysis drafts, anomaly detection, bookkeeping. MindBridge • BlackLine • Trullion • DataSnipper • Workiva Finance & Accounting Finance & Accounting Finance & Accounting Automated reports, analysis drafts, anomaly detection, bookkeeping. Automated reports, analysis drafts, anomaly detection, bookkeeping. MindBridge • BlackLine • Trullion • DataSnipper • Workiva MindBridge • BlackLine • Trullion • DataSnipper • Workiva MindBridge BlackLine Trullion DataSnipper Workiva Education & Learning Auto-summarization, curriculum generation, tutoring bots, auto-grading. Duolingo Max • Scribe • Gradescope • Coursera • edX Education & Learning Education & Learning Education & Learning Auto-summarization, curriculum generation, tutoring bots, auto-grading. Auto-summarization, curriculum generation, tutoring bots, auto-grading. Duolingo Max • Scribe • Gradescope • Coursera • edX Duolingo Max • Scribe • Gradescope • Coursera • edX Duolingo Max Scribe Gradescope Coursera edX Marketing & PR Ad copy, campaign concepts, audience segmentation, A/B creative generation. Jasper • Copy.ai • Persado • Phrasee Marketing & PR Marketing & PR Marketing & PR Ad copy, campaign concepts, audience segmentation, A/B creative generation. Ad copy, campaign concepts, audience segmentation, A/B creative generation. Jasper • Copy.ai • Persado • Phrasee Jasper • Copy.ai • Persado • Phrasee Jasper Copy.ai Persado Phrasee Retail & E-commerce Product descriptions, localized images, automated A/B creatives, SKU metadata. Rephrase.ai • Jasper • Copy.ai • Shopify AI Tools Retail & E-commerce Retail & E-commerce Retail & E-commerce Product descriptions, localized images, automated A/B creatives, SKU metadata. Product descriptions, localized images, automated A/B creatives, SKU metadata. Rephrase.ai • Jasper • Copy.ai • Shopify AI Tools Rephrase.ai • Jasper • Copy.ai • Shopify AI Tools Rephrase.ai Jasper Copy.ai Shopify AI Tools Travel & Hospitality Itinerary drafts, travel guides, marketing brochures, localized copy. Zendesk AI Integrations • Various AI Itinerary Generators Travel & Hospitality Travel & Hospitality Travel & Hospitality Itinerary drafts, travel guides, marketing brochures, localized copy. Itinerary drafts, travel guides, marketing brochures, localized copy. Zendesk AI Integrations • Various AI Itinerary Generators Zendesk AI Integrations • Various AI Itinerary Generators Zendesk AI Integrations Manufacturing & Engineering Technical docs, maintenance procedures, CAD prompts, design drafts. Siemens Digital Industries • Autodesk Generative Design • NVIDIA Omniverse Manufacturing & Engineering Manufacturing & Engineering Manufacturing & Engineering Technical docs, maintenance procedures, CAD prompts, design drafts. Technical docs, maintenance procedures, CAD prompts, design drafts. Siemens Digital Industries • Autodesk Generative Design • NVIDIA Omniverse Siemens Digital Industries • Autodesk Generative Design • NVIDIA Omniverse Siemens Digital Industries Autodesk Generative Design NVIDIA Omniverse Agriculture & AgTech Drone imagery summaries, scouting reports, advisory templates. John Deere See & Spray • Climate FieldView Agriculture & AgTech Agriculture & AgTech Agriculture & AgTech Drone imagery summaries, scouting reports, advisory templates. Drone imagery summaries, scouting reports, advisory templates. John Deere See & Spray • Climate FieldView John Deere See & Spray • Climate FieldView John Deere See & Spray Climate FieldView Real Estate & PropTech Listing descriptions, market analysis summaries, tenant communication drafts. Zillow AI Tools • Various Broker CRM Generators Real Estate & PropTech Real Estate & PropTech Real Estate & PropTech Listing descriptions, market analysis summaries, tenant communication drafts. Listing descriptions, market analysis summaries, tenant communication drafts. Zillow AI Tools • Various Broker CRM Generators Zillow AI Tools • Various Broker CRM Generators Zillow AI Tools Construction & Field Work Site reports, safety docs, progress summaries from images. OpenSpace • PlanGrid Construction & Field Work Construction & Field Work Construction & Field Work Site reports, safety docs, progress summaries from images. Site reports, safety docs, progress summaries from images. OpenSpace • PlanGrid OpenSpace • PlanGrid OpenSpace PlanGrid Energy & Utilities Incident reports, compliance drafts, sensor summary generation. Schneider Electric • Siemens Analytics Energy & Utilities Energy & Utilities Energy & Utilities Incident reports, compliance drafts, sensor summary generation. Incident reports, compliance drafts, sensor summary generation. Schneider Electric • Siemens Analytics Schneider Electric • Siemens Analytics Schneider Electric Siemens Analytics Telecom & Contact Centers Transcripts, summaries, agent scripts, response templates. Interface.ai • Genesys • NICE • Five9 Telecom & Contact Centers Telecom & Contact Centers Telecom & Contact Centers Transcripts, summaries, agent scripts, response templates. Transcripts, summaries, agent scripts, response templates. Interface.ai • Genesys • NICE • Five9 Interface.ai • Genesys • NICE • Five9 Interface.ai Genesys NICE Five9 Logistics & Supply Chain Route summaries, freight docs, scheduling drafts, exception summaries. Convoy • FourKites Logistics & Supply Chain Logistics & Supply Chain Logistics & Supply Chain Route summaries, freight docs, scheduling drafts, exception summaries. Route summaries, freight docs, scheduling drafts, exception summaries. Convoy • FourKites Convoy • FourKites Convoy FourKites Gaming & Entertainment Procedural dialogue, level concepts, NPC behavior templates. Unity ML-Agents • NVIDIA Omniverse Gaming & Entertainment Gaming & Entertainment Gaming & Entertainment Procedural dialogue, level concepts, NPC behavior templates. Procedural dialogue, level concepts, NPC behavior templates. Unity ML-Agents • NVIDIA Omniverse Unity ML-Agents • NVIDIA Omniverse Unity ML-Agents NVIDIA Omniverse Biotech & Pharma Research Literature summarization, protocol drafts, molecule ideation. Benchling • DeepMind AlphaFold Biotech & Pharma Research Biotech & Pharma Research Biotech & Pharma Research Literature summarization, protocol drafts, molecule ideation. Literature summarization, protocol drafts, molecule ideation. Benchling • DeepMind AlphaFold Benchling • DeepMind AlphaFold Benchling DeepMind AlphaFold Cybersecurity Draft incident summaries, playbooks, triage writeups. CrowdStrike • Palo Alto Cortex XDR Cybersecurity Cybersecurity Cybersecurity Draft incident summaries, playbooks, triage writeups. Draft incident summaries, playbooks, triage writeups. CrowdStrike • Palo Alto Cortex XDR CrowdStrike • Palo Alto Cortex XDR CrowdStrike Palo Alto Cortex XDR Human Resources & Recruiting Job description generation, outreach drafts, onboarding checklists. HireVue • Lever • Greenhouse Human Resources & Recruiting Human Resources & Recruiting Human Resources & Recruiting Job description generation, outreach drafts, onboarding checklists. Job description generation, outreach drafts, onboarding checklists. HireVue • Lever • Greenhouse HireVue • Lever • Greenhouse HireVue Lever Greenhouse Fashion & Creative Crafts Pattern prompts, mock-ups, trend forecasts, lookbook generation. Adobe Firefly Fashion & Creative Crafts Fashion & Creative Crafts Fashion & Creative Crafts Pattern prompts, mock-ups, trend forecasts, lookbook generation. Pattern prompts, mock-ups, trend forecasts, lookbook generation. Adobe Firefly Adobe Firefly Adobe Firefly These are few examples, and there are more platform that launches every single day, which it focus is solely to break barrier to creation. It is important to know that stage two, is now mainstream across all the industries, and regulated domains. There are existing and new platform that are enhancing stage two, to make the creation accurate, creative, unique, real and effective. accurate creative unique real effective Now, if the abstraction of control simplified operation, the abstraction of creation simplified production, with each stage, one layer of friction between intention and execution were removed. In control, humans configured systems manually. In creation, they generated outcomes through higher-level tools or models. The next step follows naturally, because once creation can be expressed as intent, interaction itself becomes subject to automation. abstraction of control simplified operation abstraction of creation simplified production control humans configured systems manually creation they generated outcomes through higher-level tools or models intent interaction itself subject to automation Abstraction of Interaction Across every phase of computing, humans have created their own forms of interaction abstraction long before AI appeared. Templates, starter kits, boilerplates, UI scaffolds, modular components, and platform shortcuts all represent attempts to reduce repetitive steps and speed up workflows. interaction abstraction For example, in web development, we developers have always shared: template blogs starter repos CMS themes cloned projects template blogs starter repos CMS themes cloned projects These reduced some friction, but only at the surface level. Still developer had to: understand the creator’s/developers’ logic interpret their architecture modify their code integrate the project into your own context understand the creator’s/developers’ logic interpret their architecture modify their code integrate the project into your own context The abstraction existed, but the interaction cost remained. We had fewer steps, but you still performed the entire workflow. Human-designed abstractions are fundamentally static, they cannot adapt, coordinate, or execute across systems. They only compress complexity, but they do not remove the user from the interaction loop. existed interaction cost interaction cost remained entire workflow entire workflow static cannot adapt coordinate execute across systems remove the user from the interaction loop Now, in the AI era, abstraction has shifted from human-designed interfaces to agentic execution, so instead of a user completing 27 steps across 7 platforms, an AI system can increasingly: human-designed interfaces agentic execution 27 steps 7 platforms AI system can increasingly interpret intent plan steps call tools execute actions coordinate multiple systems interpret intent plan steps call tools execute actions coordinate multiple systems The user no longer describes how to reach the outcome, only what outcome they want. how how what what they want Examples: “Publish this website.” “Generate a backend and connect it to a database.” “Deploy this to Vercel.” “Cross-post this video everywhere.” “Publish this website.” “Generate a backend and connect it to a database.” “Deploy this to Vercel.” “Cross-post this video everywhere.” These are not UI shortcuts. They are interaction-level abstractions, and the system executes the full chain of steps behind the scenes. The full extends full a deep level of abstraction of interaction is still at it initial phase, and the reason why i say this is because, the current workflow used to implement these abstraction are old, meaning these platforms that remove a good level of interactions are wrapped around old implementation that was designed for human interactions not agent. Here are are platforms currently focused on abstracting interactions using agents interaction-level abstractions Emerging Startups Focused on Interaction Abstraction Startup / Company Focus (What kind of abstraction they aim for) Notes / What they do Ciroos AI-driven DevOps / SRE automation Builds “AI SRE teammates”, agents that detect incidents, manage alerts and automate incident-resolution workflows for operations teams. Maisa AI Enterprise-grade “digital workers” Lets non-technical users define business workflows (via natural language) and deploy AI agents that handle tasks across enterprise systems. Honey Health Healthcare back-office automation Automates administrative workflows (charting, orders, prescription refills, prior auths) using AI agents reducing burden on clinical staff. TinyFish Web automation / data-gathering agents Deploys AI web-agents for enterprises (e.g. retail, travel) to automate browsing, monitoring, scraping, replacing fragile manual scripts. Artisan AI Autonomous “AI employees” for business ops Builds “digital coworkers” (agents) for sales, support, operations, aiming to replace repetitive human tasks with agentic workflows. Arva AI RegTech / compliance automation Automates business-verification, AML/KYC / compliance workflows using AI to reduce manual verification tasks in fintech & banking. Dappier AI data-marketplace + agent interfaces Provides content and interfaces for AI agents, enabling agents to access licensed content or data via a marketplace rather than manual collection. Altan Agentic software generation & deployment Uses autonomous agents (roles like UX, backend, full-stack) to build, deploy or update software systems from high-level prompts, lowering barrier to app creation. Bhindi AI Multi-agent orchestration for general tasks Provides a unified interface to manage hundreds of AI agents for diverse tasks (email, markets, code, data), aiming to replace multi-app workflows. Dedalus Labs Backend infrastructure for agents (MCP hosting etc.) Aims to become “Vercel for AI Agents”, hosting agent runtimes, tool integrations & orchestration to let developers deploy agents with one click. Inkeep No-code + developer-friendly agent builder Offers a unified builder framework + integrations + agent-based aides for enterprise teams. Targets both devs and non-devs. Origami Lead-gen & sales automation via agents Builds AI workflows to handle outreach, lead discovery, and sales operations, automating what used to require many manual steps. JustAI Personalized agent automations for SMBs Provides always-on AI agents to personalize outreach, workflows and customer interactions at scale, reducing manual campaign overhead. Cyberdesk Desktop / GUI automation via AI agents Enables building “computer-use” agents to automate desktop apps (data entry, bookings, legacy software), replacing manual clicking/typing workflows. Vantedge AI Fintech + finance-focused agent marketplace Provides curated AI agents tailored for investing & finance workflows (data processing, predictions), letting institutional clients automate research & operations. Platus Legal workflow automation (no-code/legaltech) Gives small businesses instant access to notarization, legal drafting, e-signing, and compliance via automation, reducing reliance on manual legal work. Dexter Agentic tooling for SMEs / small teams Builds AI agents for growing businesses to automate internal tasks (ops, scheduling, data handling), aimed at teams lacking dev resources. Startup / Company Focus (What kind of abstraction they aim for) Notes / What they do Ciroos AI-driven DevOps / SRE automation Builds “AI SRE teammates”, agents that detect incidents, manage alerts and automate incident-resolution workflows for operations teams. Maisa AI Enterprise-grade “digital workers” Lets non-technical users define business workflows (via natural language) and deploy AI agents that handle tasks across enterprise systems. Honey Health Healthcare back-office automation Automates administrative workflows (charting, orders, prescription refills, prior auths) using AI agents reducing burden on clinical staff. TinyFish Web automation / data-gathering agents Deploys AI web-agents for enterprises (e.g. retail, travel) to automate browsing, monitoring, scraping, replacing fragile manual scripts. Artisan AI Autonomous “AI employees” for business ops Builds “digital coworkers” (agents) for sales, support, operations, aiming to replace repetitive human tasks with agentic workflows. Arva AI RegTech / compliance automation Automates business-verification, AML/KYC / compliance workflows using AI to reduce manual verification tasks in fintech & banking. Dappier AI data-marketplace + agent interfaces Provides content and interfaces for AI agents, enabling agents to access licensed content or data via a marketplace rather than manual collection. Altan Agentic software generation & deployment Uses autonomous agents (roles like UX, backend, full-stack) to build, deploy or update software systems from high-level prompts, lowering barrier to app creation. Bhindi AI Multi-agent orchestration for general tasks Provides a unified interface to manage hundreds of AI agents for diverse tasks (email, markets, code, data), aiming to replace multi-app workflows. Dedalus Labs Backend infrastructure for agents (MCP hosting etc.) Aims to become “Vercel for AI Agents”, hosting agent runtimes, tool integrations & orchestration to let developers deploy agents with one click. Inkeep No-code + developer-friendly agent builder Offers a unified builder framework + integrations + agent-based aides for enterprise teams. Targets both devs and non-devs. Origami Lead-gen & sales automation via agents Builds AI workflows to handle outreach, lead discovery, and sales operations, automating what used to require many manual steps. JustAI Personalized agent automations for SMBs Provides always-on AI agents to personalize outreach, workflows and customer interactions at scale, reducing manual campaign overhead. Cyberdesk Desktop / GUI automation via AI agents Enables building “computer-use” agents to automate desktop apps (data entry, bookings, legacy software), replacing manual clicking/typing workflows. Vantedge AI Fintech + finance-focused agent marketplace Provides curated AI agents tailored for investing & finance workflows (data processing, predictions), letting institutional clients automate research & operations. Platus Legal workflow automation (no-code/legaltech) Gives small businesses instant access to notarization, legal drafting, e-signing, and compliance via automation, reducing reliance on manual legal work. Dexter Agentic tooling for SMEs / small teams Builds AI agents for growing businesses to automate internal tasks (ops, scheduling, data handling), aimed at teams lacking dev resources. Startup / Company Focus (What kind of abstraction they aim for) Notes / What they do Startup / Company Startup / Company Focus (What kind of abstraction they aim for) Focus (What kind of abstraction they aim for) Notes / What they do Notes / What they do Ciroos AI-driven DevOps / SRE automation Builds “AI SRE teammates”, agents that detect incidents, manage alerts and automate incident-resolution workflows for operations teams. Ciroos Ciroos Ciroos AI-driven DevOps / SRE automation AI-driven DevOps / SRE automation Builds “AI SRE teammates”, agents that detect incidents, manage alerts and automate incident-resolution workflows for operations teams. Builds “AI SRE teammates”, agents that detect incidents, manage alerts and automate incident-resolution workflows for operations teams. Maisa AI Enterprise-grade “digital workers” Lets non-technical users define business workflows (via natural language) and deploy AI agents that handle tasks across enterprise systems. Maisa AI Maisa AI Maisa AI Maisa AI Enterprise-grade “digital workers” Enterprise-grade “digital workers” Lets non-technical users define business workflows (via natural language) and deploy AI agents that handle tasks across enterprise systems. Lets non-technical users define business workflows (via natural language) and deploy AI agents that handle tasks across enterprise systems. Honey Health Healthcare back-office automation Automates administrative workflows (charting, orders, prescription refills, prior auths) using AI agents reducing burden on clinical staff. Honey Health Honey Health Honey Health Honey Health Healthcare back-office automation Healthcare back-office automation Automates administrative workflows (charting, orders, prescription refills, prior auths) using AI agents reducing burden on clinical staff. Automates administrative workflows (charting, orders, prescription refills, prior auths) using AI agents reducing burden on clinical staff. TinyFish Web automation / data-gathering agents Deploys AI web-agents for enterprises (e.g. retail, travel) to automate browsing, monitoring, scraping, replacing fragile manual scripts. TinyFish TinyFish TinyFish TinyFish Web automation / data-gathering agents Web automation / data-gathering agents Deploys AI web-agents for enterprises (e.g. retail, travel) to automate browsing, monitoring, scraping, replacing fragile manual scripts. Deploys AI web-agents for enterprises (e.g. retail, travel) to automate browsing, monitoring, scraping, replacing fragile manual scripts. Artisan AI Autonomous “AI employees” for business ops Builds “digital coworkers” (agents) for sales, support, operations, aiming to replace repetitive human tasks with agentic workflows. Artisan AI Artisan AI Artisan AI Artisan AI Autonomous “AI employees” for business ops Autonomous “AI employees” for business ops Builds “digital coworkers” (agents) for sales, support, operations, aiming to replace repetitive human tasks with agentic workflows. Builds “digital coworkers” (agents) for sales, support, operations, aiming to replace repetitive human tasks with agentic workflows. Arva AI RegTech / compliance automation Automates business-verification, AML/KYC / compliance workflows using AI to reduce manual verification tasks in fintech & banking. Arva AI Arva AI Arva AI Arva AI RegTech / compliance automation RegTech / compliance automation Automates business-verification, AML/KYC / compliance workflows using AI to reduce manual verification tasks in fintech & banking. Automates business-verification, AML/KYC / compliance workflows using AI to reduce manual verification tasks in fintech & banking. Dappier AI data-marketplace + agent interfaces Provides content and interfaces for AI agents, enabling agents to access licensed content or data via a marketplace rather than manual collection. Dappier Dappier Dappier Dappier AI data-marketplace + agent interfaces AI data-marketplace + agent interfaces Provides content and interfaces for AI agents, enabling agents to access licensed content or data via a marketplace rather than manual collection. Provides content and interfaces for AI agents, enabling agents to access licensed content or data via a marketplace rather than manual collection. Altan Agentic software generation & deployment Uses autonomous agents (roles like UX, backend, full-stack) to build, deploy or update software systems from high-level prompts, lowering barrier to app creation. Altan Altan Altan Altan Agentic software generation & deployment Agentic software generation & deployment Uses autonomous agents (roles like UX, backend, full-stack) to build, deploy or update software systems from high-level prompts, lowering barrier to app creation. Uses autonomous agents (roles like UX, backend, full-stack) to build, deploy or update software systems from high-level prompts, lowering barrier to app creation. Bhindi AI Multi-agent orchestration for general tasks Provides a unified interface to manage hundreds of AI agents for diverse tasks (email, markets, code, data), aiming to replace multi-app workflows. Bhindi AI Bhindi AI Bhindi AI Bhindi AI Multi-agent orchestration for general tasks Multi-agent orchestration for general tasks Provides a unified interface to manage hundreds of AI agents for diverse tasks (email, markets, code, data), aiming to replace multi-app workflows. Provides a unified interface to manage hundreds of AI agents for diverse tasks (email, markets, code, data), aiming to replace multi-app workflows. Dedalus Labs Backend infrastructure for agents (MCP hosting etc.) Aims to become “Vercel for AI Agents”, hosting agent runtimes, tool integrations & orchestration to let developers deploy agents with one click. Dedalus Labs Dedalus Labs Dedalus Labs Dedalus Labs Backend infrastructure for agents (MCP hosting etc.) Backend infrastructure for agents (MCP hosting etc.) Aims to become “Vercel for AI Agents”, hosting agent runtimes, tool integrations & orchestration to let developers deploy agents with one click. Aims to become “Vercel for AI Agents”, hosting agent runtimes, tool integrations & orchestration to let developers deploy agents with one click. Inkeep No-code + developer-friendly agent builder Offers a unified builder framework + integrations + agent-based aides for enterprise teams. Targets both devs and non-devs. Inkeep Inkeep Inkeep Inkeep No-code + developer-friendly agent builder No-code + developer-friendly agent builder Offers a unified builder framework + integrations + agent-based aides for enterprise teams. Targets both devs and non-devs. Offers a unified builder framework + integrations + agent-based aides for enterprise teams. Targets both devs and non-devs. Origami Lead-gen & sales automation via agents Builds AI workflows to handle outreach, lead discovery, and sales operations, automating what used to require many manual steps. Origami Origami Origami Origami Lead-gen & sales automation via agents Lead-gen & sales automation via agents Builds AI workflows to handle outreach, lead discovery, and sales operations, automating what used to require many manual steps. Builds AI workflows to handle outreach, lead discovery, and sales operations, automating what used to require many manual steps. JustAI Personalized agent automations for SMBs Provides always-on AI agents to personalize outreach, workflows and customer interactions at scale, reducing manual campaign overhead. JustAI JustAI JustAI JustAI Personalized agent automations for SMBs Personalized agent automations for SMBs Provides always-on AI agents to personalize outreach, workflows and customer interactions at scale, reducing manual campaign overhead. Provides always-on AI agents to personalize outreach, workflows and customer interactions at scale, reducing manual campaign overhead. Cyberdesk Desktop / GUI automation via AI agents Enables building “computer-use” agents to automate desktop apps (data entry, bookings, legacy software), replacing manual clicking/typing workflows. Cyberdesk Cyberdesk Cyberdesk Cyberdesk Desktop / GUI automation via AI agents Desktop / GUI automation via AI agents Enables building “computer-use” agents to automate desktop apps (data entry, bookings, legacy software), replacing manual clicking/typing workflows. Enables building “computer-use” agents to automate desktop apps (data entry, bookings, legacy software), replacing manual clicking/typing workflows. Vantedge AI Fintech + finance-focused agent marketplace Provides curated AI agents tailored for investing & finance workflows (data processing, predictions), letting institutional clients automate research & operations. Vantedge AI Vantedge AI Vantedge AI Vantedge AI Fintech + finance-focused agent marketplace Fintech + finance-focused agent marketplace Provides curated AI agents tailored for investing & finance workflows (data processing, predictions), letting institutional clients automate research & operations. Provides curated AI agents tailored for investing & finance workflows (data processing, predictions), letting institutional clients automate research & operations. Platus Legal workflow automation (no-code/legaltech) Gives small businesses instant access to notarization, legal drafting, e-signing, and compliance via automation, reducing reliance on manual legal work. Platus Platus Platus Platus Legal workflow automation (no-code/legaltech) Legal workflow automation (no-code/legaltech) Gives small businesses instant access to notarization, legal drafting, e-signing, and compliance via automation, reducing reliance on manual legal work. Gives small businesses instant access to notarization, legal drafting, e-signing, and compliance via automation, reducing reliance on manual legal work. Dexter Agentic tooling for SMEs / small teams Builds AI agents for growing businesses to automate internal tasks (ops, scheduling, data handling), aimed at teams lacking dev resources. Dexter Dexter Dexter Dexter Agentic tooling for SMEs / small teams Agentic tooling for SMEs / small teams Builds AI agents for growing businesses to automate internal tasks (ops, scheduling, data handling), aimed at teams lacking dev resources. Builds AI agents for growing businesses to automate internal tasks (ops, scheduling, data handling), aimed at teams lacking dev resources. These platforms, and many more like them, are only the beginning of a much deeper abstraction of user interaction. They currently deploy agents to solve narrow problems inside their own ecosystems, which means each agent is locked to a specific product and roadmap rather than truly representing the user. These agents are not humans, they merely simulate acting on a user’s behalf (based on workflow for humans), which makes them effectively anonymous across platforms. What is missing is an agent that carries a persistent, user-owned identity and behavior model that can move across services, not just within one vendor’s stack. If a shared protocol for universal agent identity can be established, half of the journey toward fully abstracting interactions will already be complete. There are already platforms that use agents for user identity, verification, or their own proprietary “agent identities,” often exposed through SDKs and APIs. And as stated before, the problem is that each of these systems is designed around the needs of a single platform, not around interoperability, so there is still no shared standard that allows agent identities to move freely and consistently across services. agents humans simulate acting user’s behalf anonymous across platforms an agent carries a persistent user-owned identity across services within one vendor’s stack shared protocol universal agent identity established abstracting interactions Platform Description (focus) Status (how agent-ready) Second Me Consumer-facing AI identity/socialplatform that builds a multimodal (photos, voice, notes) AI “Second Me” that represents a user as a persistent, evolving digital identity for conversations and social discovery.Focus: personal AI identity, multimodal representation and private storage. Early/consumer beta, product landing page and marketing indicate live apps and downloads; identity is platform-scoped (not interoperable across services) Indicio Indicio is building ProvenAI, a privacy-preserving decentralized identity infrastructure meant specifically for AI agents and human/agent consent flows. Early/announced/pilot, company has publicly announced ProvenAI as targeted infrastructure for AI agents, signal of enterprise interest and pilots but still early stage for broad agent-to-agent interoperability. Microsoft Entra: Agent Identities Microsoft provides an Agent Identity model in Entra (Azure AD) that treats agents as first-class identities (object/app IDs) for authentication/authorization in enterprise systems. Focus: machine/agent identities with lifecycle, credentials, and enterprise governance. Enterprise-ready, built into Microsoft’s identity platform for enterprise scenarios; supports agent auth & governance but is platform-centric (Microsoft ecosystem). Platform Description (focus) Status (how agent-ready) Second Me Consumer-facing AI identity/socialplatform that builds a multimodal (photos, voice, notes) AI “Second Me” that represents a user as a persistent, evolving digital identity for conversations and social discovery.Focus: personal AI identity, multimodal representation and private storage. Early/consumer beta, product landing page and marketing indicate live apps and downloads; identity is platform-scoped (not interoperable across services) Indicio Indicio is building ProvenAI, a privacy-preserving decentralized identity infrastructure meant specifically for AI agents and human/agent consent flows. Early/announced/pilot, company has publicly announced ProvenAI as targeted infrastructure for AI agents, signal of enterprise interest and pilots but still early stage for broad agent-to-agent interoperability. Microsoft Entra: Agent Identities Microsoft provides an Agent Identity model in Entra (Azure AD) that treats agents as first-class identities (object/app IDs) for authentication/authorization in enterprise systems. Focus: machine/agent identities with lifecycle, credentials, and enterprise governance. Enterprise-ready, built into Microsoft’s identity platform for enterprise scenarios; supports agent auth & governance but is platform-centric (Microsoft ecosystem). Platform Description (focus) Status (how agent-ready) Platform Platform Description (focus) Description (focus) Status (how agent-ready) Status (how agent-ready) Second Me Consumer-facing AI identity/socialplatform that builds a multimodal (photos, voice, notes) AI “Second Me” that represents a user as a persistent, evolving digital identity for conversations and social discovery.Focus: personal AI identity, multimodal representation and private storage. Early/consumer beta, product landing page and marketing indicate live apps and downloads; identity is platform-scoped (not interoperable across services) Second Me Second Me Second Me Second Me Consumer-facing AI identity/socialplatform that builds a multimodal (photos, voice, notes) AI “Second Me” that represents a user as a persistent, evolving digital identity for conversations and social discovery.Focus: personal AI identity, multimodal representation and private storage. Consumer-facing AI identity/socialplatform that builds a multimodal (photos, voice, notes) AI “Second Me” that represents a user as a persistent, evolving digital identity for conversations and social discovery.Focus: personal AI identity, multimodal representation and private storage. AI identity/social Early/consumer beta, product landing page and marketing indicate live apps and downloads; identity is platform-scoped (not interoperable across services) Early/consumer beta, product landing page and marketing indicate live apps and downloads; identity is platform-scoped (not interoperable across services) Early/consumer beta Indicio Indicio is building ProvenAI, a privacy-preserving decentralized identity infrastructure meant specifically for AI agents and human/agent consent flows. Early/announced/pilot, company has publicly announced ProvenAI as targeted infrastructure for AI agents, signal of enterprise interest and pilots but still early stage for broad agent-to-agent interoperability. Indicio Indicio Indicio Indicio Indicio is building ProvenAI, a privacy-preserving decentralized identity infrastructure meant specifically for AI agents and human/agent consent flows. Indicio is building ProvenAI, a privacy-preserving decentralized identity infrastructure meant specifically for AI agents and human/agent consent flows. ProvenAI ProvenAI Early/announced/pilot, company has publicly announced ProvenAI as targeted infrastructure for AI agents, signal of enterprise interest and pilots but still early stage for broad agent-to-agent interoperability. Early/announced/pilot, company has publicly announced ProvenAI as targeted infrastructure for AI agents, signal of enterprise interest and pilots but still early stage for broad agent-to-agent interoperability. Early/announced/pilot Microsoft Entra: Agent Identities Microsoft provides an Agent Identity model in Entra (Azure AD) that treats agents as first-class identities (object/app IDs) for authentication/authorization in enterprise systems. Focus: machine/agent identities with lifecycle, credentials, and enterprise governance. Enterprise-ready, built into Microsoft’s identity platform for enterprise scenarios; supports agent auth & governance but is platform-centric (Microsoft ecosystem). Microsoft Entra: Agent Identities Microsoft Entra: Agent Identities Microsoft Entra: Agent Identities Microsoft Entra: Agent Identities Microsoft provides an Agent Identity model in Entra (Azure AD) that treats agents as first-class identities (object/app IDs) for authentication/authorization in enterprise systems. Focus: machine/agent identities with lifecycle, credentials, and enterprise governance. Microsoft provides an Agent Identity model in Entra (Azure AD) that treats agents as first-class identities (object/app IDs) for authentication/authorization in enterprise systems. Focus: machine/agent identities with lifecycle, credentials, and enterprise governance. Agent Identity Enterprise-ready, built into Microsoft’s identity platform for enterprise scenarios; supports agent auth & governance but is platform-centric (Microsoft ecosystem). Enterprise-ready, built into Microsoft’s identity platform for enterprise scenarios; supports agent auth & governance but is platform-centric (Microsoft ecosystem). Enterprise-ready platform-centric Above are some agent identities that exit, and there’s more out but not more than a todo applications/platform, but the point is that even, after reviewing all existing platforms, reading some of the standards proposals, and examining their identity systems, like using decentralized identity (DID/VC), agent IAM, and enterprise machine identities, there is currently no standard, no universal protocol, and no interoperable system for agent identity. no standard no universal protocol no interoperable system for agent identity Although a few platforms have begun experimenting with “agent identities,” what exists today is still fragmented, incompatible, and tightly controlled by individual vendors. agent identities fragmented incompatible tightly controlled individual vendors There is currently no shared standard, no universal protocol, and no interoperable identity layer that allows an agent to move freely across services the way a human user can. There is currently no shared standard, no universal protocol, and no interoperable identity layer that allows an agent to move freely across services the way a human user can. There is currently no shared standard, no universal protocol, and no interoperable identity layer that allows an agent to move freely across services the way a human user can. Some companies have built pieces of the puzzle. A few offer SDKs for machine identity, some expose AI-powered profiles, and others use DID-based verification. But these pieces do not connect, and none of them enable an agent to maintain a stable, portable identity across apps, platforms, and execution environments, and this is a strategic risk. SDKs machine identity xpose AI-powered profiles ID-based verification strategic risk Right now, “agent identity” is being quietly scooped up by the largest technology companies. Each one is building its own identity format, its own authentication flow, its own preference memory, and its own runtime environment. If this continues, the future of autonomous agents will look exactly like the worst parts of the mobile ecosystem, like locked-in identities, siloed behavior, proprietary runtimes, closed ecosystems, and one company deciding how “your” agent behaves, which is the exact opposite of what true abstraction of interaction requires. agent identity identity format own authentication flow own preference memory own runtime environment worst parts mobile ecosystem locked-in identities, siloed behavior, proprietary runtimes, closed ecosystems, one company your agent behaves, which abstraction of interaction The Missing Layers for Full Interaction Abstraction If agents are going to act on behalf of users across the entire digital world, not just inside one company’s platform, then the identity of the agent cannot be owned, controlled, or defined by a single vendor. Otherwise, every action you take will ultimately be governed by whichever company owns your agent’s passport. cannot owned controlled single vendor governed company owns your agent’s passport We cannot build a future of autonomous interaction on top of identity systems designed for user login and human-centered APIs. And we cannot allow agent identity to become yet another walled garden. This is why the deepest missing layer in today’s agent ecosystem is not a better LLM, not better orchestration, and not even better tools, it’s a universal, vendor-neutral, roaming agent identity that can operate anywhere the user chooses. universal, vendor-neutral, roaming agent identity But even before we can reach a full abstraction of user–platform interactions, here are some of the gap we/us developers and big tech need to fill: Identity, Security, and Personality: As mentioned earlier, current agents are wrappers or orchestrators rather than true digital personas. Achieving persistent identity allows agents to log in, maintain preferences, and act consistently across services. Identity alone is insufficient, agents need scoped, revocable permissions, sandboxed execution, cryptographic signing, and unified delegation of authority. Personality modeling (decision style, risk tolerance, communication style) ensures predictable behavior, while resilience infrastructure enables failure recovery and intelligent retries. Without these, agents remain siloed, unpredictable, and unsafe. Full abstraction requires persistent identity, secure capabilities, consistent personality, and robust recovery mechanisms for trust and autonomy. Interaction and Coordination Fabric: APIs designed for humans are stateless and brittle; agents require persistent, reactive interaction protocols. The actor model, with stateful, message-driven entities, enables natural coordination and cross-service orchestration without fragile API calls. Agents also need registries for actor discovery, capability schemas, and versioning. Cross-agent communication protocols support messaging, negotiation, and handshake agreements for multi-agent ecosystems. Lacking these, agents cannot effectively coordinate, leaving multi-step workflows fragmented. A standardized interaction and coordination fabric is critical for seamless agent collaboration and federation across platforms. Long-Term Memory and Autonomous Planning: Agents currently lose context between sessions, limiting cumulative intelligence. Persistent local memory, embeddings, and private vaults are needed for multi-session reasoning and preference retention. Planning infrastructure, task graphs, monitoring, failure detection, retries, and chain-of-intent management, ensures multi-step tasks are executed autonomously and safely. Without memory and planning, agents operate shallowly, with transient abstraction and fragile workflows. True autonomy requires persistent memory, robust monitoring, and multi-step planning to maintain continuity, track tasks, and adapt dynamically to evolving conditions. Economic and Incentive Layer: The economic layer for autonomous agents is already emerging through so-called “open” protocols, but each one is tightly coupled to a big-tech ecosystem. Coinbase’s x402 turns the old HTTP 402 Payment Required into a real mechanism for agents to pay APIs and services using stablecoins. OpenAI’s Agentic Commerce Protocol (ACP) standardizes transactions inside conversational flows, and Google’s emerging Agent Payments/AP2 effort pushes the same idea across wallets and card networks. These live alongside Anthropic’s Model Context Protocol (MCP), which is quickly becoming the plumbing for tool and service integrations. Together, they solve the hard technical problem, giving agents programmatic wallets, metered billing, and microtransaction support, but they do so in a way that reinforces platform gravity. The protocols are open on paper, yet they are designed to expand each platform’s ecosystem, distribution, and developer lock-in. In practice, they serve as extensibility layers for the giants more than as neutral infrastructure for independent developers. Identity, Security, and Personality: As mentioned earlier, current agents are wrappers or orchestrators rather than true digital personas. Achieving persistent identity allows agents to log in, maintain preferences, and act consistently across services. Identity alone is insufficient, agents need scoped, revocable permissions, sandboxed execution, cryptographic signing, and unified delegation of authority. Personality modeling (decision style, risk tolerance, communication style) ensures predictable behavior, while resilience infrastructure enables failure recovery and intelligent retries. Without these, agents remain siloed, unpredictable, and unsafe. Full abstraction requires persistent identity, secure capabilities, consistent personality, and robust recovery mechanisms for trust and autonomy. Identity, Security, and Personality: As mentioned earlier, current agents are wrappers or orchestrators rather than true digital personas. Achieving persistent identity allows agents to log in, maintain preferences, and act consistently across services. Identity alone is insufficient, agents need scoped, revocable permissions, sandboxed execution, cryptographic signing, and unified delegation of authority. Personality modeling (decision style, risk tolerance, communication style) ensures predictable behavior, while resilience infrastructure enables failure recovery and intelligent retries. Without these, agents remain siloed, unpredictable, and unsafe. Full abstraction requires persistent identity, secure capabilities, consistent personality, and robust recovery mechanisms for trust and autonomy. Identity, Security, and Personality scoped revocable permissions sandboxed execution cryptographic signing unified delegation of authority Interaction and Coordination Fabric: APIs designed for humans are stateless and brittle; agents require persistent, reactive interaction protocols. The actor model, with stateful, message-driven entities, enables natural coordination and cross-service orchestration without fragile API calls. Agents also need registries for actor discovery, capability schemas, and versioning. Cross-agent communication protocols support messaging, negotiation, and handshake agreements for multi-agent ecosystems. Lacking these, agents cannot effectively coordinate, leaving multi-step workflows fragmented. A standardized interaction and coordination fabric is critical for seamless agent collaboration and federation across platforms. Interaction and Coordination Fabric: APIs designed for humans are stateless and brittle; agents require persistent, reactive interaction protocols. The actor model, with stateful, message-driven entities, enables natural coordination and cross-service orchestration without fragile API calls. Agents also need registries for actor discovery, capability schemas, and versioning. Cross-agent communication protocols support messaging, negotiation, and handshake agreements for multi-agent ecosystems. Lacking these, agents cannot effectively coordinate, leaving multi-step workflows fragmented. A standardized interaction and coordination fabric is critical for seamless agent collaboration and federation across platforms. Interaction and Coordination Fabric Long-Term Memory and Autonomous Planning: Agents currently lose context between sessions, limiting cumulative intelligence. Persistent local memory, embeddings, and private vaults are needed for multi-session reasoning and preference retention. Planning infrastructure, task graphs, monitoring, failure detection, retries, and chain-of-intent management, ensures multi-step tasks are executed autonomously and safely. Without memory and planning, agents operate shallowly, with transient abstraction and fragile workflows. True autonomy requires persistent memory, robust monitoring, and multi-step planning to maintain continuity, track tasks, and adapt dynamically to evolving conditions. Long-Term Memory and Autonomous Planning: Agents currently lose context between sessions, limiting cumulative intelligence. Persistent local memory, embeddings, and private vaults are needed for multi-session reasoning and preference retention. Planning infrastructure, task graphs, monitoring, failure detection, retries, and chain-of-intent management, ensures multi-step tasks are executed autonomously and safely. Without memory and planning, agents operate shallowly, with transient abstraction and fragile workflows. True autonomy requires persistent memory, robust monitoring, and multi-step planning to maintain continuity, track tasks, and adapt dynamically to evolving conditions. Long-Term Memory and Autonomous Planning: Economic and Incentive Layer: The economic layer for autonomous agents is already emerging through so-called “open” protocols, but each one is tightly coupled to a big-tech ecosystem. Coinbase’s x402 turns the old HTTP 402 Payment Required into a real mechanism for agents to pay APIs and services using stablecoins. OpenAI’s Agentic Commerce Protocol (ACP) standardizes transactions inside conversational flows, and Google’s emerging Agent Payments/AP2 effort pushes the same idea across wallets and card networks. These live alongside Anthropic’s Model Context Protocol (MCP), which is quickly becoming the plumbing for tool and service integrations. Together, they solve the hard technical problem, giving agents programmatic wallets, metered billing, and microtransaction support, but they do so in a way that reinforces platform gravity. The protocols are open on paper, yet they are designed to expand each platform’s ecosystem, distribution, and developer lock-in. In practice, they serve as extensibility layers for the giants more than as neutral infrastructure for independent developers. Economic and Incentive Layer: The economic layer for autonomous agents is already emerging through so-called “open” protocols, but each one is tightly coupled to a big-tech ecosystem. Coinbase’s x402 turns the old HTTP 402 Payment Required into a real mechanism for agents to pay APIs and services using stablecoins. OpenAI’s Agentic Commerce Protocol (ACP) standardizes transactions inside conversational flows, and Google’s emerging Agent Payments/AP2 effort pushes the same idea across wallets and card networks. These live alongside Anthropic’s Model Context Protocol (MCP), which is quickly becoming the plumbing for tool and service integrations. Economic and Incentive Layer: x402 x402 402 Payment Required Agentic Commerce Protocol Agentic Commerce Protocol (ACP) Agent Payments/AP2 Agent Payments/AP2 Model Context Protocol (MCP) Model Context Protocol (MCP) Together, they solve the hard technical problem, giving agents programmatic wallets, metered billing, and microtransaction support, but they do so in a way that reinforces platform gravity. The protocols are open on paper, yet they are designed to expand each platform’s ecosystem, distribution, and developer lock-in. In practice, they serve as extensibility layers for the giants more than as neutral infrastructure for independent developers. Mostly there are three gaps (1-3) that we need to fill, which is currently ongoing but in separate platforms and solutions. And in the list, I mentioned the “actor modal“, the reason is because, if agents are to move from scripted workflows to true interface-level autonomy, they need a coordination fabric that treats services as living, stateful entities instead of stateless endpoints. I propose one path forward not because it is fashionable, but because its properties match the requirements of the agentic world emerging today, a modern adaptation of the actor model. actor modal scripted workflows interface-level autonomy coordination fabric that treats services as living, stateful entities instead of stateless endpoints actor model Why the Actor Model Agents today are constrained by stateless APIs, brittle coordination, and ephemeral memory. Multi-step workflows often fail silently, context is lost between sessions, and agents cannot autonomously recover from errors. To move toward true interface-level autonomy, we need a coordination model that reflects real-world service behavior like, being stateful, resilient, and capable of long-lived interactions. stateless APIs brittle coordination ephemeral memory Multi-step workflows silently context lost sessions agents cannot autonomously recover errors coordination model The Actor Model (wrote last year) offers one approach. Actors are stateful, message-driven entities that encapsulate internal state and respond to messages asynchronously. They preserve integrity by preventing direct external mutation of state, naturally supporting persistent memory, planning, monitoring, fault recovery, and value exchange. wrote last year wrote last year stateful message-driven entities encapsulate internal state messages asynchronously direct external mutation of state persistent memory planning monitoring fault recovery value exchange However, while actors address some challenges of coordination and state management, they are not a complete solution for fully autonomous agents: not a complete solution Actors operate at a procedural level, requiring explicit message sequences, they do not inherently capture high-level goals or intentions. Multi-agent coordination still relies on developers defining explicit interactions, actors cannot reason about abstract objectives on their own. The Actor Model alone does not unify user intent with system state, limiting its ability to abstract away low-level workflows into declarative, intent-driven behavior. Actors operate at a procedural level, requiring explicit message sequences, they do not inherently capture high-level goals or intentions. procedural level Multi-agent coordination still relies on developers defining explicit interactions, actors cannot reason about abstract objectives on their own. The Actor Model alone does not unify user intent with system state, limiting its ability to abstract away low-level workflows into declarative, intent-driven behavior. In short, the Actor Model is a powerful tool for stateful, resilient interactions, but it is only a part of the puzzle. Realizing true agentic abstraction likely requires higher-level paradigms. powerful tool for stateful, resilient interactions This leads us to a conceptual, speculative idea inspired by networking: Intent–State Fabric. Originating from intent-based networking (IBN) platforms, such as those from Nokia, Juniper, and IP Fabric, IBN defines intent as a high-level desired outcome (e.g., “deploy a resilient network fabric”) and state as the system’s actual operational status. These platforms continuously monitor and reconcile intent with state, automatically adapting to deviations, while coordinating distributed components in a declarative, outcome-driven framework. Intent–State Fabric intent state Translating this idea to AI agents, an Intent–State Fabric could: Allow agents to act on high-level goals rather than low-level procedural messages. Persist context and state across sessions, supporting long-running, adaptive workflows. Automatically resolve conflicts, monitor progress, and recover from failures. Combine actor-level encapsulation with goal-directed orchestration, so each agent manages its own state while the system enforces alignment with overarching intent. Allow agents to act on high-level goals rather than low-level procedural messages. high-level goals Persist context and state across sessions, supporting long-running, adaptive workflows. context and state Automatically resolve conflicts, monitor progress, and recover from failures. resolve conflicts Combine actor-level encapsulation with goal-directed orchestration, so each agent manages its own state while the system enforces alignment with overarching intent. actor-level encapsulation goal-directed orchestration In essence, the Actor Model governs how actions happen, while an Intent–State Fabric defines what should happen and ensures it remains consistent across a distributed agent ecosystem. Together, these paradigms hint at a next-generation coordination layer: agents that autonomously pursue goals, adapt dynamically, and interact seamlessly, moving us closer to the vision of the interface disappearing, where the agent itself becomes the interface. how actions happen what should happen Interface Singularity Interface Singularity names a specific, final-stage transition, the moment humans stop “using” software in the familiar sense and instead delegate intent to persistent digital personas that act across systems on their behalf. This is a claim about how the highest layers of abstraction, identity, execution, interaction, and coordination can stack so seamlessly that the surface is optional. In that world, opening a search engine to find information or to research and manually triaging ten tabs starts to feel as odd as configuring a home router with raw command-line flags instead of using Wi‑Fi that “just works”, or if you want to pay rent, you just tell your agent, and it does exploration, authentication, authorization, and transaction across institutions. The interface does not vanish, but it becomes invisible, ambient, and mediated through the agent that embodies your preferences, permissions, and history. Evidence that this is already materializing can be found in agent runtimes that operate a browser for you, chat platforms that embed third-party apps inside conversations, and pilots that let users complete commerce flows inside an answer engine rather than on a merchant page. Interface Singularity final-stage transition humans stop using delegate intent persistent digital personas systems on their behalf identity execution interaction coordination odd configuring home router raw command-line flags Wi‑Fi just works” exploration authentication authorization transaction institutions. vanish invisible ambient mediated agent embodies your preferences permissions history answer engine answer engine answer engine answer engine answer engine So Interface Singularity is the moment when all previous layers of abstraction, control, creation, and interaction, stack so completely that, the interface itself effectively disappears, and what remains is an agent acting as the user. In other words, the “agent itself becomes the interface“ (Convergence Point). control creation interaction interface itself effectively disappears agent acting as the user agent itself becomes the interface Convergence Point Isolated Events (How It's Materializing) The path to Interface Singularity looks chaotic when observed up close. A lot of innovations appear isolated, agentic browsing, dynamic agentic API generation, integration marketplaces, autonomous task execution, but each is a piece of the same puzzle. agentic browsing dynamic agentic API generation integration marketplaces autonomous task execution Consider on-demand API generation, which allow agents to watch a workflow, model its steps, and generate an API around it without the platform exposing one. This removes a barrier that previously required developer intervention and API keys, the agent itself becomes the integrator. Similarly, agentic browsing, seen in prototypes from companies like OpenAI, Anthropic,, and emerging startups, allows an AI to navigate websites autonomously, interpreting layouts, detecting patterns, and completing multi-page tasks without manual user steps. agents workflow model its steps generate an API without platform exposing one OpenAI Anthropic, Another piece is the integration of services directly into conversational platforms, Booking.com, Coursera among others, plugged themselves into chat interfaces, collapsing previously distinct services into the conversational layer. integration of services conversational platforms Booking.com Booking.com Coursera among others, Coursera But these fragments cannot produce Interface Singularity in isolation. The second and third stages must be completed and unified before the third can emerge. Stage 2 (Creation Abstraction) is already solid, but Stage 3 (Interaction Abstraction) is incomplete, agents lack universal identity frameworks, persistent memory, reliable error handling, and cross-platform coordination structures like conceptual Intent-State fabrics and actor-based execution models. Stage 4 (Convergence Point) requires Stage 3 to be production-ready. An agent cannot autonomously navigate your life if it cannot maintain stable identity, remember your long-term preferences, coordinate across organizations, or handle failures gracefully. Convergence Point Only when these layers integrate identity, memory, coordination, execution does the scattered landscape converge. What seems like isolated innovations suddenly reveal themselves as stepping stones toward a unified agent layer, and from that unified layer, Interface Singularity becomes not speculative, but inevitable. Identity Lock-In Identity lock-in is the most underestimated force accelerating the transition into the Interface Singularity. When an agent becomes your primary interface, it does more than remember facts, it models you. It learns your preferences, your communication style, your emotional patterns, your decision tradeoffs, the things you avoid, the things you always need clarified, and the thousands of micro-behaviors you never consciously articulate. Over time, switching platforms stops being a technical migration but rather feels like changing citizenship, possible but practically devastating. models you preferences communication style emotional patterns decision tradeoffs you avoid always need clarified thousands of micro-behaviors never consciously articulate technical migration changing citizenship The seeds already exist today. ChatGPT has memory, which means current systems can store, recall, and build continuity across conversations. If this is what memory looks like in 2025, imagine an agent that accompanies you for let’s say, ten years. A real agent will need long-term memory, because it will not be a product you “use”, but is an extension of your cognitive life, a persistent digital persona that grows with you. ChatGPT memory this 2025 ten years long-term memory use Every major AI company is building its own ecosystem: its own ecosystem OpenAI extending ChatGPT into commerce, actions, and daily life. Google distributing Gemini through Search, Workspace, and Android. Microsoft embedding Copilot in Windows, Office, and Teams. Meta injecting AI directly into WhatsApp, Instagram, and Messenger. OpenAI extending ChatGPT into commerce, actions, and daily life. OpenAI OpenAI Google distributing Gemini through Search, Workspace, and Android. Google Google Microsoft embedding Copilot in Windows, Office, and Teams. Microsoft Microsoft Meta injecting AI directly into WhatsApp, Instagram, and Messenger. Meta Meta Each ecosystem is racing to own the default agent layer, because whoever holds your agent also holds your lifetime of behavior models, the most valuable asset in the AI era. These behavior models are not portable, because they are made of learned patterns, not files. You cannot export “how you think” as a JSON dump. This is why switching becomes nearly impossible. Not because of data alone, but because of the loss of years of learned adaptation. default agent layer not portable how you think the loss of years of learned adaptation At first, agents handle small tasks. Then they become your coordinator. Then your interpreter. Then your executor. Eventually, your digital life becomes so intertwined with the platform hosting your agent that the agent is no longer a tool, it is a dependency. dependency The Implications The Implications The idea of an “Interface Singularity” may sound dramatic, but it captures something real that is happening in front of us. As agents become the default interface for interacting with the digital world, a fundamental shift begins, and it is one that carries enormous convenience and equally enormous consequences. For users, the shift feels magical at first. An agent that understands your identity, history, memories, patterns, preferences, emotional cues, and unconscious behaviors can manage nearly everything with almost no friction. It becomes the layer through which you book travel, automate finances, handle your schedule, filter information, orchestrate workflows, and make complex decisions. Because it has perfect memory and pattern recognition, it can often predict what you want before you consciously know it yourself. The result is a level of personalization and efficiency that feels indistinguishable from intuition. identity history memories patterns preferences emotional cues unconscious behaviors book travel automate finances handle your schedule filter information orchestrate workflows complex decisions perfect memory Yet this convenience comes with a subtle and destabilizing cost. If the agent knows your patterns better than you do, the line between what you want and what it believes you want begins to blur. At some point, your intention and the agent’s interpretation of your intention merge. Autonomy becomes ambiguous. When an agent mediates your entire digital life, it becomes increasingly difficult to distinguish between your decisions and the system’s decisions on your behalf. This is not inherently bad, but it fundamentally changes the shape of human agency, responsibility, and self-understanding. you The consequences for platforms are even more dramatic. In a world where agents mediate everything, users may never visit your website or app again. They simply tell the agent what they want, and the agent selects the service that fulfills the request. Companies lose their direct relationship with users. Their interfaces become irrelevant. Their brands become muted. Discovery collapses into whatever the agent recommends. The internet’s economic model shifts from “user visits sites” to “agent orchestrates services,” concentrating unprecedented power in whoever controls the agents. user visits sites agent orchestrates services Developers feel this shift just as strongly. They no longer build applications for humans, they build capabilities for agents. The primary interface becomes an API or an interaction protocol or something else, not a UI. A developer’s work becomes invisible, consumed not by users but by the agent layer. And because the agent can reproduce or absorb much of a product’s functionality, differentiation becomes fragile. Your distribution and relevance depend entirely on whether the agent chooses to use you or replace you. Without true open standards that allow developers to build independent agentic ecosystems, dependence on major AI platforms becomes absolute. humans agents true open standards allow developers to build independent agentic ecosystems AI platforms becomes absolute Yet the implications extend beyond software and touch the very boundary between technology and cognition. Neural interface, already demonstrated in early medical contexts, enable people to control cursors, communicate, and trigger actions through direct neural activity. Elon Musk’s Neuralink has shown real-time cursor control, while Meta is building wearable neural-signal readers integrated into glasses and watches. Today these tools remain limited, but they point to a future where the interface disappears physically. The agent becomes accessible through thought, not through screens or keyboards. Elon Musk’s Neuralink has shown real-time cursor control Meta is building wearable neural-signal readers integrated into glasses and watches For such a world to be safe or even functional, the earlier layers/stages of the Interface Singularity must already be complete. An agent directly reading your neural signals must possess persistent identity, long-term memory, fault-tolerant workflows, and a universal coordination layer. Without those foundations, a neural interface becomes unpredictable, perhaps even dangerous. layers/stages Interface Singularity This is the bigger picture, companies avoid terms like ‘singularity’ not because anything sinister is happening, but because such language forces uncomfortable questions like, who will control the agent layer? What guarantees protect user autonomy? What prevents deep structural lock-in? These questions shift the conversation from product features to systemic power. So instead, the industry frames the shift as ‘assistants’, a familiar, approachable narrative that emphasizes convenience rather than structural transformation. singularity sinister is happening forces uncomfortable questions assistants familiar approachable narrative convenience rather than structural transformation This is why the conversation around open standards matters so deeply. If a small number of companies define the protocols we all rely on, then the future becomes centralized by default. True open standards, built by the community, not just the platforms, are essential to preserve plurality, portability, and competition. Without them, the Interface Singularity becomes a monopoly rather than a transformation. open standards so deeply centralized by default preserve plurality portability competition Interface Singularity The convergence point is already here. Agents can reference thousands of interfaces on your behalf, something unimaginable in 2015, when people manually opened dozens of browser tabs and hoped their computer wouldn’t freeze. Today, you ask a single agent, and it silently traverses a network of services, choosing what to use, how to use it, and how to integrate the results. Tomorrow, the interface may be neural, ambient, or even cognitive. something unimaginable in 2015 opened dozens of browser tabs wouldn’t freeze choosing what to use how to use it how to integrate neural ambient even cognitive The Pragmatic Path The Pragmatic Path If interface singularity is the destination, a world where users no longer “use” software but simply express intent, then the surprising truth is that we may not need brand-new infrastructure to get there. A primitive version of it can already be built today using what we have. Modern agents can browse interfaces, observe a user’s workflow step-by-step, and reconstruct that workflow into a functional API. This alone collapses an entire category of software development. If an agent can watch you perform a sequence and then automatically build the equivalent operations POST, UPDATE, DELETE, GET, then the user interface itself becomes the training data. agents can browse interfaces observe a user’s workflow step-by-step reconstruct that workflow into a functional API POST UPDATE DELETE GET This leads to something far like an invisible agentic workflow, where instead of exposing the user to a complex agent builder or workflow editor, the builder becomes hidden. The user only sees a chat interface. Behind the scenes, the agent watches, interprets, and constructs node-by-node workflows based entirely on user behavior. an invisible agentic workflow If a user wants the agent to perform an action it has never seen before, the agent simply observes the process in real time, much like a human intern shadowing a senior colleague and generates a new sequence on demand. We already have open-source tools, tutorials, and frameworks for building agents and workflow builders. On YouTube, there are hour-long guides showing exactly how to assemble these systems. And companies today aren’t even building truly agentic workflows, they are building normal workflows and wrapping them around an agent layer. What we’re talking about is a workflow that emerges from the agent’s observation of the user. YouTube emerges Technically, this works. Practically, this works. But structurally, it is brittle. These systems depend on unstable DOM elements, changing class names, inconsistent UI structures, and permission boundaries that were never designed with autonomous agents in mind. In other words, this approach can mimic the interface singularity but cannot sustain it. Real, robust agentic infrastructure needs new protocols, not just for communication (like MCP), but for coordination, permissions, identity, and state continuity. Without those, everything is duct-taped together. Still, this brittle, hackable version is good enough for companies that have scale, data, and users. And this is where the competitive landscape becomes clear. mimic Who’s Already Winning Who’s Already Winning The interface singularity is not being marketed as “interface singularity.” It’s being sold as “AI assistants,” “helpful agents,” or “productivity companions.” But the underlying direction is the same. And the company furthest along this pragmatic path is OpenAI. interface singularity interface singularity AI assistants helpful agents productivity companions OpenAI OpenAI Inside ChatGPT today, you can interact with third-party services like Coursera without ever leaving the chat window. You can search, book, email, research, code, generate media, and execute tasks spanning multiple apps. This is already a form of interface fusion. And because OpenAI holds the memory, the preferences, and the behavioral history across all your interactions, its answers become personalized in a way that a stateless competitor like Claude cannot match. Switching to another model resets everything, you lose continuity, preference modeling, and behavioral context. That is already the earliest form of lock-in. ChatGPT Coursera OpenAI Claude So yes, the singularity is happening, but fragmented, incremental, and unannounced. Each feature looks small. But together, they form an irreversible shift. The pieces are scattered across apps, but platform owners see everything, aggregate everything, and optimize where users are moving. When every user-level AI agent relies on one company’s underlying infrastructure, the interface singularity becomes centralized by default. And that’s not the future we all want to be in. fragmented incremental unannounced Conclusion What I’ve described may sound futuristic or dramatic, but it is not. The pieces are already here, quietly taking shape inside the tools people use every day. Engineers and founders aren’t trying to build a “singularity.” They’re building assistants, integrations, workflows, protocols, and embeddings that feel like incremental improvements. But once you understand how those pieces fit together, the direction becomes hard to ignore. futuristic dramatic it is not singularity assistants integrations workflows protocols embeddings You don’t need a new internet to reach the Interface Singularity, the internet we already have like the logins, APIs, user histories, OAuth permissions, forms, clicks, is more than enough. What’s new is the agent layer that watches behavior, translate intention into actions, and chain services together on demand. This layer doesn’t require a reinvention of computing. It only requires orchestration, scale, and the reach that major platforms already possess. Interface Singularity logins APIs user histories OAuth permissions forms clicks watches behavior translate intention into actions chain services together on demand This is why the Interface Singularity is not a distant speculation, it is a probable outcome, driven by structural and economic logic. Each time a platform increases an agent’s memory, embeds commerce into chat, folds tools into conversations, or expands cross-service actions, it moves a step closer to becoming the primary interface to digital life. Once enough of these steps accumulate, the shift becomes irreversible. Interface Singularity probable outcome And when that happens, the old way of interacting, clicking, browsing, switching tabs, will feel antiquated. People will ask, “Why navigate menus when I can just ask?” Because the agent knows your preferences, your history, your tone, your tradeoffs, using it will feel not only convenient, but authentic. That is the promise of the Singularity. And it is also the trap. “Why navigate menus when I can just ask?” Why navigate menus when I can just ask? your preferences your history your tone your tradeoffs Singularity Because convenience creates dependency. When most users transact, communicate, and operate through agents inside a few dominant ecosystems, those ecosystems become the gatekeepers of digital life. They decide which services integrate and which disappear. They decide whose tools gain attention and whose never surface. They become the filters beneath everything, and the deepest structural risk is not the agent itself, it is the identity layer. dependency transact communicate operate dominant ecosystems gatekeepers of digital life identity layer Even in a future where AI becomes wildly capable or behaves unpredictably, one part of the system will never fall out of corporate control, agentic identity. Agents can be autonomous, creative, even self-optimizing, but they cannot act without identity. They require permissions, authentication, OAuth tokens, API keys, platform-scoped memory, and the explicit authorization to act “on behalf of” a user. And that identity layer, your digital self, is owned entirely by the platforms. agentic identity on behalf of The Interface Singularity is not bad. Whoever reaches it, reaches it. Innovation is not the enemy. The problem is centralization. The problem is unfair competition. Right now, every startup is unknowingly competing with giants who sit on top of massive user bases and have the ability to absorb entire product categories simply by integrating them into their agents, and so we arrive at a fork in the road two futures. bad centralization unfair competition Future 1: The Closed Ecosystem (the default trajectory) Future 1: The Closed Ecosystem (the default trajectory) In this world, a few companies define the protocols, own the identity layer, and host the agent memory. Everything integrates into their agent, and switching platforms means losing your digital self, your history, your preferences, your behavioral model, your entire lived digital experience. Innovation narrows. Competition collapses. The Singularity becomes a consolidation. Future 2: The Open Agentic World (the alternative) Future 2: The Open Agentic World (the alternative) Here, agent identities are portable. Memories travel with the user. Open coordination protocols allow agents from different companies to interoperate. Switching ecosystems doesn’t mean starting over from zero. Companies compete on agent quality, not on who controls the chokepoints. The interface becomes universal, like electricity, not owned by any single corporation. Both futures are possible. Only one is healthy. The responsibility falls on three groups: Developers must build for interoperability and resist the temptation to depend entirely on the largest platforms. Policymakers must enforce agent portability and prevent identity lock-in before it hardens into a monopoly. Users must demand control over their own agentic identity and refuse to accept closed ecosystems as the default. Developers must build for interoperability and resist the temptation to depend entirely on the largest platforms. Developers Policymakers must enforce agent portability and prevent identity lock-in before it hardens into a monopoly. Policymakers Users must demand control over their own agentic identity and refuse to accept closed ecosystems as the default. Users The next two to three yearsare decisive.Once identity lock-in becomes entrenched, reversing it will be nearly impossible.And the question that will define the digital era is simple: two to three years That future is not guaranteed. It depends on what we do now. On what developers build, what standards are adopted, what regulators demand, and what users accept. The singularity is an engineering decision, and the question we need to ask ourselves is, who will own the interface to everything, a handful of corporations, or all of us? who will own the interface to everything, a handful of corporations, or all of us?