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It passed CI. It passed your evals. The customer still got the wrong answer
AI agents frequently return 'successful' responses that are factually wrong, necessitating end-to-end trace debugging to identify retrieval or orchestration failures.
What: Sean O'Dell argues that traditional CI/CD pipelines fail to catch logic errors in AI agents because standard tests don't validate retrieval context validity. Debugging now requires capturing end-to-end traces including retrieval inputs, model calls, and tool results, rather than just relying on faithfulness metrics.
Why it matters: The industry is moving from testing inputs/outputs to auditing the 'trajectory' of an agent, as '200 OK' statuses mask quiet failures where the model retrieves relevant but incorrect context.
Takeaway: Add `release`, `retrieval_config`, and `feature_flag` state as attributes at the root span of your AI agent's traces to enable comparison between successful and failed runs.
Deep dive
- Agent failures are often 'quiet'—the system returns 200 OK while the user receives a wrong answer.
- A 'faithful' answer can be wrong if the retrieved documentation context is irrelevant to the specific user requirements (e.g., wrong product version).
- Trace data must link model calls, tool arguments, and tool results to explain why an agent followed a specific path.
- Repeated, identical tool calls in a trace often indicate harness-level retries or orchestration logic issues, not model failures.
- Evaluation needs to distinguish between 'relevance' (does the document contain the topic?) and 'validity' (is it correct for this request?).
- Implement separate deterministic tests for data lookup filters—do not rely on a judge model for logic that can be verified with standard unit tests.
Decoder
- Faithfulness (Groundedness): A metric measuring if an AI's answer is strictly supported by the provided source documents.
- Trajectory: The sequence of model thoughts, tool calls, and tool outputs that form an agent's decision path.
- Root Span: The top-level operation in a distributed trace that represents the entire user request.
Original article
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