Explore how observability powers agent evaluation in AI systems, key strategies, and tools for optimizing agent performance.
César Miguelañez
As artificial intelligence systems grow more advanced, the way we build, monitor, and improve them is evolving. The rise of agentic AI - AI systems capable of reasoning, decision-making, and interacting autonomously - brings both opportunities and challenges. Unlike traditional software, agentic systems are nondeterministic and exhibit emergent behavior, making observability and evaluation vital to their success in production environments.
This article explores how to effectively observe and evaluate agentic AI systems, diving into the concepts, techniques, and workflows used by experts to ensure reliability, quality, and continuous improvement.
Why Agent Observability and Evaluation Matter
Agentic AI systems are fundamentally different from traditional software or even single-call LLM applications. While conventional software operates deterministically, agents introduce unpredictability due to multi-step reasoning, tool usage, and complex decision-making sequences.
Key Differences Between Traditional Software and Agentic AI:
Deterministic vs. Nondeterministic Behavior: Traditional software behaves the same way for the same input, while agents can produce different outputs due to randomness and prompt sensitivity.
Code vs. Emergent Logic: In agentic systems, much of the logic emerges dynamically as the agent runs, rather than being explicitly encoded in the program's source code.
Debugging Code vs. Debugging Reasoning: Debugging for agents involves understanding failures in reasoning, tool usage, and context interpretation rather than isolating bugs in a fixed codebase.
Observability and evaluation help manage these complexities. Observability ensures you understand what your agents are doing at every step, while evaluation allows you to systematically measure and improve their performance through rigorous tests and monitoring.
Understanding Agent Observability: Building Blocks and Tools
Agent observability is about tracking and understanding an agent’s behavior. The core elements of observability include runs, traces, and threads, each capturing a different level of detail.
The Primitives of Agent Observability:
Runs: A single execution step, such as one LLM call. This includes inputs, outputs, and parameters (e.g., temperature settings).
Traces: A sequence of runs captured during a complete agent execution. This provides a full view of the agent's reasoning and actions from start to finish.
Threads: Groupings of traces that include human interventions, such as multi-turn conversations. Threads capture continuity across interactions for more complex evaluations.
Applications of Observability:
Debugging reasoning failures by examining individual traces and runs.
Monitoring agents in production to catch issues in real-time.
Supporting offline evaluations by generating data sets from production traces.
Evaluating Agentic AI: Moving Beyond Software Testing
Evaluation plays a critical role in improving agentic AI systems. Unlike traditional software testing, where the focus is on verifying deterministic code paths, agent evaluation centers on assessing reasoning, decision-making, and behavior over time.
Levels of Agent Evaluation:
Single-Step Evaluations: Test whether an individual LLM call produces the expected output. This is useful for validating isolated reasoning steps or tool usage.
Full-Turn Evaluations: Assess the agent's end-to-end behavior within a single execution cycle. For example, did a coding agent successfully implement and test a function?
Multi-Turn Evaluations: Examine the agent’s behavior across multiple conversational or interaction turns. This includes tracking memory, context retention, and follow-up actions.
Challenges Unique to Agent Evaluation:
Emergent Behavior: Agents often exhibit unexpected capabilities or errors that are difficult to anticipate during development.
Production Insights: Many failure modes only become apparent after deployment, meaning real-world usage is critical for identifying gaps in testing.
Metrics Development: Unlike fixed benchmarks in traditional software, agentic AI requires custom metrics tailored to the application's use case and logic.
Bridging Observability and Evaluation
Observability powers evaluation by providing the data and insights needed to identify, analyze, and address issues. By logging traces and runs, developers can turn real-world data into actionable improvements.
How Observability Enables Evaluation:
Debugging Production Issues: Observability allows teams to trace errors back to their root causes, such as a faulty tool call or misinterpreted context.
Building Data Sets for Offline Evaluations: Production traces often reveal common user inputs and failure patterns, which can be transformed into test cases.
Online Evaluations: Traces from live traffic support automated evaluations to flag anomalies, identify inefficiencies, and monitor quality over time.
Workflow for Improving Agentic AI Systems
The process of improving agentic AI is iterative and data-driven. Below is a practical step-by-step workflow:
Step 1: Enable Tracing and Observability
Set up a system to log agent runs, traces, and threads for every execution. This data becomes the foundation for analysis and evaluation.
Step 2: Use Agents for Real Tasks
Deploy your agent in a controlled environment or to a small cohort of users. This generates real-world data showcasing how the agent performs on meaningful tasks.
Step 3: Review and Debug Traces
Manually inspect traces to identify failure modes, inefficiencies, or unexpected behaviors. Pay close attention to the agent’s first actions, tool usage, and reasoning steps.
Step 4: Develop Evaluation Metrics
Based on observed failures, define custom metrics for single-step, full-turn, or multi-turn evaluations. Focus on both correctness and alignment with business goals.
Step 5: Iterate on Prompts and Configuration
Refine prompts, tool descriptions, and middleware configurations to address identified issues. For example, add a checklist middleware to ensure agents verify their outputs before declaring a task complete.
Step 6: Scale Testing with Automation
As data accumulates, use automation to analyze traces at scale and categorize failure patterns. Build automated evaluations that flag recurring issues and track metrics over time.
Evaluating and Optimizing Prompts
Prompt optimization is a key lever for improving agent performance. The process involves distilling insights from traces, incorporating human priors, and packaging reusable knowledge into specialized prompts or "skills."
Techniques for Prompt Optimization:
Iterative Refinement: Analyze failures in reasoning or tool usage, then modify prompts to address specific gaps.
Skill Creation: Develop modular prompts that handle recurring tasks or patterns, such as planning workflows or verifying outputs.
Reflective Analysis: Use agent-generated reports to propose improvements for prompts and configurations.
Key Takeaways
Observability and evaluation are essential for managing the complexity of agentic AI systems, where emergent behavior and reasoning failures are common.
Tracing is the cornerstone of observability, capturing detailed data on runs, traces, and threads to support debugging, evaluation, and optimization.
Evaluation requires custom metrics tailored to your application, with a focus on reasoning, tool usage, and multi-turn behavior.
Production data is invaluable for identifying what to test, as many failure modes only emerge in real-world usage.
A proven workflow for improvement combines manual debugging, prompt refinement, and automated evaluations to iteratively enhance agent performance.
Prompt optimization and middleware hooks can significantly improve agent reliability, ensuring better alignment with business objectives.
Final Thoughts
The shift toward agentic AI systems is reshaping how we approach application development. By focusing on observability and evaluation, teams can navigate the challenges of nondeterministic behavior and achieve continuous improvement. Whether you're a product manager striving for quality or an engineer developing robust workflows, the insights and techniques outlined here provide a foundation for success in the agentic AI era.
Embrace observability, test rigorously, and iterate boldly - your agents will thrive in production.
Source: "Building Better AI Agents: Observability and Evaluation" - LangChain, YouTube, Jan 1, 1970 - https://www.youtube.com/watch?v=reISMhbZ2XE
