Learn how to optimize agent observability and evaluation to enhance LLM-based applications and ensure reliable performance.
César Miguelañez
As the integration of AI agents becomes more prevalent in production systems, understanding observability and evaluation practices has never been more critical. These foundational principles differ drastically from traditional software engineering due to the non-deterministic nature of AI systems, particularly those driven by language models (LLMs). In this article, we’ll explore how observability and evaluation empower teams to ensure AI quality, reliability, and continuous improvement.
Whether you're a product manager overseeing AI quality or a technical practitioner implementing AI features, this guide will provide actionable insights for mastering agent-based systems.
Why Observability and Evaluation Are Unique in AI Systems
Traditional software systems operate in a deterministic fashion - given the same inputs, they produce the same outputs every time. You can thoroughly test software before it goes to production, anticipating failures through predefined scenarios. AI-powered systems, especially those using agents, defy this paradigm.
The Shift in Behavior with LLMs
In AI-powered systems:
LLMs do not behave deterministically. The same input may result in different outputs due to inherent variability.
Agents take this variability further by performing actions, such as making API calls, invoking tools, or generating plans dynamically. The behavior of agents emerges during runtime and is influenced by both user input and the agent’s decision-making process.
This introduces new challenges:
Unpredictable Outputs: You cannot fully anticipate how an agent will behave until it interacts with users in production.
Unconstrained Inputs: User inputs, typically in natural language, are open-ended and unstructured, adding complexity to debugging and evaluation.
The Core Concepts of Agent Observability
In traditional software engineering, observability focuses on monitoring code execution and diagnosing failures through stack traces or performance metrics. For AI agents, the source of truth is not the code but the traces of interactions - real-time logs that capture the agent’s decision-making process.
Key Observability Primitives
Runs: The smallest execution unit in an agent’s workflow, often corresponding to a single LLM call. Each run includes:
Input Context: System prompts, tools defined, and prior interactions.
Output: The AI-generated response, tool invocation, or reasoning step.
Traces: A sequence of runs that represent the entire execution flow of an agent. Traces enable teams to:
Understand how earlier steps influence later decisions.
Diagnose errors by examining the full history of actions.
Threads: Multi-turn interactions between the user and the agent, encompassing multiple traces. Threads provide a holistic view of the agent’s performance across conversations.
Debugging via Observability
To debug effectively:
Trace Exploration: Analyze runs and traces to identify failure points, especially during LLM calls.
Context Tracking: Understand how context shifts within a conversation, as earlier inputs may cause downstream errors.
Error Source Identification: Pinpoint the reasoning or decision-making steps where the agent deviates from expected behavior.
Evaluation Strategies for AI Agents
Agent evaluation differs significantly from traditional software testing. In software, testing focuses on code paths. In agents, evaluation targets the reasoning and decision-making process, which depends on the context rather than fixed logic.
Types of Evaluations
Single-Step Evaluations:
Test the output of individual runs (e.g., a single LLM call).
Useful for verifying isolated decisions or outputs.
Pros: Fast to execute, clear pass/fail criteria.
Cons: May become outdated if the agent’s logic evolves.
Trace Evaluations:
Validate complete end-to-end agent execution, including tool invocations and state changes.
Pros: Captures the full workflow; tests real-world scenarios.
Cons: Defining metrics for complex traces can be challenging.
Thread Evaluations:
Assess multi-turn interactions to evaluate long-term context retention, coherence, and behavior consistency.
Pros: Most realistic testing for production-like scenarios.
Cons: Difficult to define both inputs and success criteria due to variability.
Offline vs. Online Evaluation
Offline Evaluation: Conducted before deployment using predefined datasets of inputs and expected outputs. Ideal for:
Catching regressions during development.
Benchmarking the agent’s capabilities over time.
Online Evaluation: Performed in production by analyzing live traces. While ground truth labels are unavailable, online evaluation helps identify:
Efficiency issues (e.g., excessive looping or unnecessary tool usage).
Failures in reasoning or trajectory.
Ad Hoc Evaluation: Exploratory analysis performed in response to specific issues (e.g., user feedback). This approach is invaluable for discovering unexpected failure modes.
The Interdependence of Observability and Evaluation
In AI agents, observability and evaluation are tightly coupled, unlike their more distinct roles in traditional software. Here’s how they work together:
Production Insights Inform Offline Evaluation:
Observability captures traces of real-world interactions, revealing gaps and errors.
Developers use these traces to build datasets for offline testing, ensuring future iterations address known issues.
Traces Enhance Debugging:
Debugging workflows rely heavily on traces to reproduce and analyze failures.
By identifying problematic runs or decisions, teams can refine agent logic and improve performance.
Online Evaluation Boosts Real-Time Feedback:
Continuous monitoring of production traces enables teams to flag and address issues proactively.
Ad Hoc Analysis Uncovers Patterns:
Exploratory techniques allow teams to detect usage trends, identify recurring errors, and improve the agent’s overall robustness.
Actionable Best Practices for Teams
To implement effective observability and evaluation workflows for AI agents:
Track Everything: Capture complete traces of every interaction to ensure comprehensive observability.
Integrate Multi-Level Testing: Combine single-step, trace, and thread evaluations to gain a robust understanding of agent behavior.
Leverage Production Data: Use real-world traces to inform offline evaluations and refine benchmarks.
Automate Where Possible: Develop automated evaluators for both offline and online testing to streamline workflows.
Encourage Collaboration: Facilitate collaboration between product teams and technical practitioners to align on quality standards and evaluation goals.
Key Takeaways
AI systems are inherently non-deterministic, making observability and evaluation essential for ensuring quality and reliability.
Observability focuses on runs, traces, and threads, with traces serving as the primary source of truth.
Agent evaluation targets reasoning and decision-making rather than traditional software code paths.
Offline evaluations catch regressions, while online evaluations flag real-time issues in production.
Traces bridge the gap between observability and evaluation, powering debugging, testing, and insights.
A multi-level evaluation strategy combining single-step, trace, and thread testing is critical for robust agent performance.
Collaboration between product managers and engineers is key to driving continuous improvement in AI systems.
Conclusion
Building reliable AI agents requires a paradigm shift in how teams approach observability and evaluation. By adopting the right tools and frameworks, such as robust tracing mechanisms and multi-level evaluation strategies, teams can navigate the challenges of non-deterministic systems with confidence. The integration of observability and evaluation not only ensures better performance but also fosters a culture of continuous learning and improvement.
Embracing these principles will empower teams to deliver AI-powered products that are not only innovative but also dependable, ensuring a seamless experience for end-users. By investing in these practices, AI teams can overcome the inherent complexities of agent-based systems and unlock their full potential.
Source: "Observability and Evals for AI Agents: A Simple Breakdown" - LangChain, YouTube, Feb 17, 2026 - https://www.youtube.com/watch?v=FDVdLrloFOw
