Agent Observatory: Building a Fail-Open Observability Primitive for AI Agents In this interview, we speak with Darshan Parmar about Agent Observatory — a lightweight, infrastructure-level observability library designed for AI agents and real-time streaming systems. The conversation explores why traditional observability approaches fall short for agent workflows, the importance of fail-open design, and how usefulness is measured without relying on vanity metrics. In this interview, we speak with Darshan Parmar about Agent Observatory — a lightweight, infrastructure-level observability library designed for AI agents and real-time streaming systems. The conversation explores why traditional observability approaches fall short for agent workflows, the importance of fail-open design, and how usefulness is measured without relying on vanity metrics. What does Agent Observatory do, and why is now the right time for it to exist? Agent Observatory is a lightweight, fail-open observability library for AI agents and real-time streaming workflows. It provides structured tracing primitives that integrate with OpenTelemetry without owning storage, UI, or global state, allowing developers to understand agent behavior in production systems without introducing new failure modes. Now is the right time for this to exist because AI agents are moving beyond prototypes into long-running, stateful production environments. As systems become more autonomous and asynchronous, debugging becomes harder, and teams need observability that is reliable, minimal, and infrastructure-agnostic rather than tightly coupled to centralized platforms. What traction has Agent Observatory seen so far? Agent Observatory is an early-stage open-source project with modest but growing traction. Adoption today is primarily through GitHub and PyPI, with usage driven by backend and platform engineers experimenting with agent workflows, voice pipelines, and streaming systems. Rather than end-user traffic, traction is reflected through open-source engagement — issues, discussions, and direct feedback from developers integrating the library into real systems. Who does Agent Observatory serve, and what’s exciting about these users? The project is built for backend and platform engineers working on AI agents, voice systems, and real-time or streaming applications. These users often operate systems that are long-running, failure-prone, and latency-sensitive, where traditional observability tools can introduce too much overhead or operational risk. What’s exciting about this audience is that they care deeply about reliability and ownership. They want tools that integrate cleanly into existing infrastructure, don’t require vendor lock-in, and don’t compromise system stability just to gain visibility. What technologies were used to build Agent Observatory, and why? The core stack is based on Python and OpenTelemetry to ensure broad compatibility and alignment with existing observability standards. Async execution models and lightweight tracing primitives were prioritized to maintain a fail-open architecture that doesn’t interfere with the runtime behavior of real-time or streaming applications. The goal was not to invent a new observability ecosystem, but to fit cleanly into the ones teams already use. Agent Observatory was recently evaluated under HackerNoon’s Proof of Usefulness framework and scored 56 (Agent Observatory POU report). How do you feel about the result? Agent Observatory POU report) Agent Observatory POU report) The evaluation was useful as an external signal, especially because the framework emphasizes real utility over projections or pitch decks. A score is less important than the criteria behind it — usefulness, adoption potential, and technical soundness. As an infrastructure project, usefulness often compounds over time as more systems integrate it. If the project were re-evaluated in the future, the strongest improvements would likely come from deeper real-world adoption rather than surface-level feature expansion. What excites you most about Agent Observatory’s potential usefulness? Most observability solutions assume a centralized platform, UI, or storage layer. What excites me about Agent Observatory is its ability to stay out of the way — providing just enough structure to make agent behavior debuggable while remaining fail-open and infrastructure-agnostic. That restraint makes it practical for production systems where reliability and ownership matter more than dashboards. What’s the most concrete evidence that people genuinely need what you’ve built? The strongest signal is how easily Agent Observatory integrates into existing OpenTelemetry pipelines without requiring architectural changes. Teams can add it to long-running agent or voice workflows and immediately gain trace visibility without introducing new points of failure. For production systems, the ability to observe behavior without risking stability is often the deciding factor. How do you distinguish genuine users from one-time “tourists”? Because this is an infrastructure library, adoption isn’t measured by sign-ups. Instead, it’s reflected in repeated usage, follow-up questions, and continued engagement over time. Signals like recurring discussions, incremental contributions, and requests for clarification are more meaningful than raw download counts. How are developers integrating Agent Observatory today? Most integrations treat Agent Observatory as a thin instrumentation layer alongside existing tracing and logging setups. It doesn’t replace existing observability tooling; it complements it. This approach lowers the barrier to experimentation, especially in production environments where teams are cautious about introducing new dependencies. As an open-source project, how do you encourage community contributions? The project keeps its scope intentionally small and its abstractions explicit. Contributors don’t need to learn a platform or UI model — just tracing semantics and failure behavior. That clarity makes it easier for contributors to understand where they can add value without navigating unnecessary complexity. Can you explain how a “fail-open” design benefits long-running voice or streaming pipelines? In long-running voice or streaming systems, observability must never block execution. A fail-open design ensures that if tracing or instrumentation fails, the system continues operating normally. This prevents cascading failures such as audio dropouts, latency spikes, or session termination — risks that are unacceptable in real-time environments. Looking ahead, what’s next for Agent Observatory? The focus is on deeper real-world usage rather than expanding the surface area. Over time, feedback from teams running production agent systems will guide refinements to the core primitives, ensuring the library remains useful, minimal, and reliable as agent architectures evolve. Final note Agent Observatory is intentionally not a platform, a SaaS, or a dashboard. It is a small infrastructure primitive designed to solve a specific, growing problem: making AI agent behavior observable without compromising production systems.

Between Two Computer Monitors: This story includes an interview between the writer and guest/interviewee. 

Agent Observatory Earns a 56 Proof of Usefulness Score by Making AI Agents Observable Without Risk

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