AI BriefingHow to Debug and Evaluate AI Agents Using Observability: A LangChain Guide
Key point
Because AI agents follow a non-deterministic reasoning process, they require a different approach to observability and evaluation than traditional software.
Details
In traditional software, code is the Source of Truth, but AI agents are different. Agents call LLMs and tools in a loop, making judgments on their own to perform tasks, so you must debug the Reasoning process rather than the code.
Agent Observability is fundamentally different from observability in traditional software.
- Non-deterministic nature: Results can vary even with the same input, so behavior cannot be predicted from code logic alone.
- Complex execution paths: The key is identifying at which point in an execution process spanning hundreds of steps the reasoning went astray.
- Importance of context: Beyond simple service call logs, you need to understand the prompt and context that led to a particular decision.
Therefore, agent engineering must become a process of completing an iterative improvement loop through Tracing and Evaluation.
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