When people think of generative AI, many envision it working as part of a “system of engagement” — a customer service agent, a supply chain management tool, or a way to intelligently interact with and search an organization’s PDFs and other proprietary data. That’s an accurate view – for the next year or two, applications that intelligently create content by leveraging large language models (LLMs) will remain a primary AI focus for enterprises. But consider this: Most of the code written at enterprises sits within business processes — systems like inventory planning, which sit between the engagement layer and the more rigid systems of record (an organization’s data, etc.) How will GenAI benefit that layer? How can an organization improve its business processes with this pervasive technology? Agentic AI is the answer. While AI agents are built to do specific tasks or automate specific, often repetitive tasks (like updating your calendar), they generally require human input. Agentic AI is all about autonomy (think self-driving cars), employing a system of agents to constantly adapt to dynamic environments and independently create, execute, and optimize results. When agentic AI is applied to business process workflows, it can replace fragile, static business processes with dynamic, context-aware automation systems. Let’s take a look at why integrating AI agents into enterprise architectures marks a transformative leap in the way organizations approach automation and business processes, and what kind of platform is required to support these systems of automation. What Agents Are Doing Now When you provide an agent with context, the agent then feeds that context to an LLM and asks it to complete and respond to it. AI agents can also use capabilities to complete tasks on behalf of users. These AI agents can perform several key functions guided by instructions and information derived from context: Tool use — The agent uses external functions, APIs, or tools to extend its capabilities and perform specific tasks. This can include calling predefined functions or interfacing with external services (like making web requests using cURL or accessing RESTful APIs) to obtain context or execute actions beyond its inherent functionalities. Decision-making — The agent evaluates available information and selects the most appropriate action to achieve its goals. This involves analyzing context, weighing possible outcomes, and choosing a course of action that aligns with the desired objectives. Planning — The agent formulates a sequence of actions or strategies to achieve a specific goal. Reasoning — The agent analyzes the available context, draws conclusions, predicts outcomes of actions, and makes informed decisions about the optimal steps to take to reach the desired outcome. These latter kinds of functions – decision-making, planning, and reasoning – often involve multiple agents working together toward a goal. The agents could seek to refine generated code for correctness, debate whether an agentic decision is biased, or plan the use of other agentic capabilities to complete a task. Orchestrating an Agentic Network Models that power networks of agents are essentially stateless functions that take context as an input and output a response, so some kind of framework is necessary to orchestrate them. Part of that orchestration could be simple refinements (the model requests more information). This might sound analogous to retrieval-augmented generation (RAG) – and it should, because RAG is essentially a simplified form of agent architecture: It provides the model with a single tool that gets it additional information, often from a vector database. But multiagentic model frameworks take this further; they broker requests for additional information or provide a response that’s designed to be fed to another agent for refinements. One agent could, say, write some Python, while another then reviews it. Or an agent could express a goal or idea, and then a second agent’s job could be to break that up into a set of tasks or review the idea to find problems that the first agent can review, and then refine the idea. Your results start to get better and better. OK, but How Do I Build Agentic AI? In the near future, a lot of software engineers will become agentic process authors. They’ll build these processes by mixing and matching components: models, user input, goals – and critical business services. An example of one of these components is the stock in an inventory management system. What if you connected that system to an agent that could help optimize inventory levels during the holiday season? With the help of another agent who has done some historical inventory level analysis, you could ensure that there is just enough inventory to meet seasonal demand yet leave little inventory after the holiday rush. This might disappoint fervent after-Christmas sale shoppers, but it would also help prevent retailers from selling their wares at a loss. But how will developers build these systems? Agentic processes can, of course, be expressed with code, but it also helps to visualize them as “agentic flows” – one agent’s output becomes the input of another agent, and so on. Tools available now are already providing a lot of value in this effort to simplify building agentic systems. One such solution is Langflow, a visual, low-code builder for creating agentic AI applications and complex AI workflows by dragging and dropping different components, without the need for much coding. Langflow enables developers to define anything as a tool, including components like a prompt, data source, model, APIs, tools, or any other agents. We’ve recently seen significant demand for building “flows” with agents, as developers are creating a lot of applications that include several multiagent capabilities: Agents are the most popular type of component that developers are inserting into flows with Langflow. Wrapping up: From copilot to Pilot Agentic workflows bring together enterprise data, AI, and APIs, forming systems of automation that empower domain experts to scale their abilities and make enterprises work better through AI. Integrating AI agents into enterprise architectures marks a big leap in how organizations approach automation and business processes. These agents, empowered by LLMs and agentic frameworks, transcend traditional boundaries by seamlessly operating across processes, workflows, and code. Adopting agentic workflows promises to enhance efficiency, scalability, and responsiveness across business operations. They’ll manage entire workflows, handle complex tasks with greater adaptability, and improve customer experiences by providing more personalized and timely interactions. As automation becomes embedded in enterprise systems, AI copilots will graduate to pilots, and organizations that employ agentic AI will be better positioned to innovate, compete, and deliver value. For more details on agentic AI, read the free whitepaper, “Systems of Automation: The Future of Enterprise Architecture Is Agentic.” And check out this page to learn more about Langflow. Written by Ed Anuff, chief product officer, DataStax When people think of generative AI, many envision it working as part of a “system of engagement” — a customer service agent, a supply chain management tool, or a way to intelligently interact with and search an organization’s PDFs and other proprietary data. That’s an accurate view – for the next year or two, applications that intelligently create content by leveraging large language models (LLMs) will remain a primary AI focus for enterprises. leveraging large language models (LLMs) leveraging large language models (LLMs) But consider this: Most of the code written at enterprises sits within business processes — systems like inventory planning, which sit between the engagement layer and the more rigid systems of record (an organization’s data, etc.) How will GenAI benefit that layer? How can an organization improve its business processes with this pervasive technology? Agentic AI is the answer. While AI agents are built to do specific tasks or automate specific, often repetitive tasks (like updating your calendar), they generally require human input. Agentic AI is all about autonomy (think self-driving cars), employing a system of agents to constantly adapt to dynamic environments and independently create, execute, and optimize results. When agentic AI is applied to business process workflows, it can replace fragile, static business processes with dynamic, context-aware automation systems. Let’s take a look at why integrating AI agents into enterprise architectures marks a transformative leap in the way organizations approach automation and business processes, and what kind of platform is required to support these systems of automation. What Agents Are Doing Now When you provide an agent with context, the agent then feeds that context to an LLM and asks it to complete and respond to it. AI agents can also use capabilities to complete tasks on behalf of users. These AI agents can perform several key functions guided by instructions and information derived from context: Tool use — The agent uses external functions, APIs, or tools to extend its capabilities and perform specific tasks. This can include calling predefined functions or interfacing with external services (like making web requests using cURL or accessing RESTful APIs) to obtain context or execute actions beyond its inherent functionalities. Decision-making — The agent evaluates available information and selects the most appropriate action to achieve its goals. This involves analyzing context, weighing possible outcomes, and choosing a course of action that aligns with the desired objectives. Decision-making — Planning — The agent formulates a sequence of actions or strategies to achieve a specific goal. Planning — Reasoning — The agent analyzes the available context, draws conclusions, predicts outcomes of actions, and makes informed decisions about the optimal steps to take to reach the desired outcome. Reasoning — These latter kinds of functions – decision-making, planning, and reasoning – often involve multiple agents working together toward a goal. The agents could seek to refine generated code for correctness, debate whether an agentic decision is biased, or plan the use of other agentic capabilities to complete a task. Orchestrating an Agentic Network Models that power networks of agents are essentially stateless functions that take context as an input and output a response, so some kind of framework is necessary to orchestrate them. Part of that orchestration could be simple refinements (the model requests more information). This might sound analogous to retrieval-augmented generation (RAG) – and it should, because RAG is essentially a simplified form of agent architecture: It provides the model with a single tool that gets it additional information, often from a vector database. retrieval-augmented generation (RAG) retrieval-augmented generation (RAG) But multiagentic model frameworks take this further; they broker requests for additional information or provide a response that’s designed to be fed to another agent for refinements. One agent could, say, write some Python, while another then reviews it. Or an agent could express a goal or idea, and then a second agent’s job could be to break that up into a set of tasks or review the idea to find problems that the first agent can review, and then refine the idea. Your results start to get better and better. OK, but How Do I Build Agentic AI? In the near future, a lot of software engineers will become agentic process authors. They’ll build these processes by mixing and matching components: models, user input, goals – and critical business services. An example of one of these components is the stock in an inventory management system. What if you connected that system to an agent that could help optimize inventory levels during the holiday season? With the help of another agent who has done some historical inventory level analysis, you could ensure that there is just enough inventory to meet seasonal demand yet leave little inventory after the holiday rush. This might disappoint fervent after-Christmas sale shoppers, but it would also help prevent retailers from selling their wares at a loss. But how will developers build these systems? Agentic processes can, of course, be expressed with code, but it also helps to visualize them as “agentic flows” – one agent’s output becomes the input of another agent, and so on. Tools available now are already providing a lot of value in this effort to simplify building agentic systems. One such solution is Langflow , a visual, low-code builder for creating agentic AI applications and complex AI workflows by dragging and dropping different components, without the need for much coding. Langflow Langflow Langflow enables developers to define anything as a tool, including components like a prompt, data source, model, APIs, tools, or any other agents. We’ve recently seen significant demand for building “flows” with agents, as developers are creating a lot of applications that include several multiagent capabilities: Agents are the most popular type of component that developers are inserting into flows with Langflow. Wrapping up: From copilot to Pilot Agentic workflows bring together enterprise data, AI, and APIs, forming systems of automation that empower domain experts to scale their abilities and make enterprises work better through AI. Integrating AI agents into enterprise architectures marks a big leap in how organizations approach automation and business processes. These agents, empowered by LLMs and agentic frameworks, transcend traditional boundaries by seamlessly operating across processes, workflows, and code. Adopting agentic workflows promises to enhance efficiency, scalability, and responsiveness across business operations. They’ll manage entire workflows, handle complex tasks with greater adaptability, and improve customer experiences by providing more personalized and timely interactions. As automation becomes embedded in enterprise systems, AI copilots will graduate to pilots, and organizations that employ agentic AI will be better positioned to innovate, compete, and deliver value. For more details on agentic AI, read the free whitepaper, “Systems of Automation: The Future of Enterprise Architecture Is Agentic.” And check out this page to learn more about Langflow. For more details on agentic AI, read the free whitepaper, “ Systems of Automation: The Future of Enterprise Architecture Is Agentic. ” And check out this page to learn more about Langflow . Systems of Automation: The Future of Enterprise Architecture Is Agentic. Systems of Automation: The Future of Enterprise Architecture Is Agentic. this page this page to learn more about Langflow Written by Ed Anuff, chief product officer, DataStax Written by Ed Anuff, chief product officer, DataStax

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

Behind AI Agents: The Infrastructure That Supports Autonomy

DataStax Astra DB is the vector database for getting GenAI apps into production, fast. Try it for free today!

Too Long; Didn't Read

Make resilience your competitive advantage

AI Agents: How They're Getting Ready to Blow Up the Business Process Layer

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

3 Key Tools for Deploying AI/ML Workloads on Kubernetes

Getting to Know Google's Agent2Agent Protocol

What is Agentic Workflow? Learn More About the Microservices of AI

The Future Protocol Powering AI’s Mind-Meld? MCP

Beep Beep Bop Bop: How to Deploy Multiple AI Agents Using Local LLMs

3 Key Tools for Deploying AI/ML Workloads on Kubernetes

Getting to Know Google's Agent2Agent Protocol

What is Agentic Workflow? Learn More About the Microservices of AI

The Future Protocol Powering AI’s Mind-Meld? MCP

Beep Beep Bop Bop: How to Deploy Multiple AI Agents Using Local LLMs

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps