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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchStart with a complete trace of one failing run, then find the first step that diverged from expected behavior. That point is usually more useful than the final bad answer: it helps distinguish a repeated tool call from a timeout, a bad tool result, or a generation error. Reproduce the run with its original inputs and configuration, change one plausible cause at a time, and preserve confirmed failures as regression tests.
Capture a reproducible run before changing anything
A trace is evidence of what happened; by itself, it does not prove why the model behaved that way. To make comparisons meaningful, preserve the conditions of a single failure rather than changing several variables between runs.
- Save the exact input and instructions: include the user request and relevant system or developer instructions.
- Record the setup: note the model and configuration, available tools and their schemas, relevant state or memory, and environment or version details.
- Capture every event: record model steps, handoffs, tool names and arguments, tool results or errors, retries, and timestamps or durations.
- Protect sensitive data: redact secrets and personal or otherwise sensitive user information before storing or sharing traces. Apply appropriate access and retention controls.
Then compare the trace with the expected plan, tool sequence, or output and identify the earliest incorrect decision. A later mistake may simply be a consequence of that first divergence.
Debug the symptom visible in the trace
Use the symptom to decide what to inspect first, not to assume a cause. Confirm each hypothesis against the run.
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If the agent keeps looping
Inspect the sequence of calls and state changes, not only the final transcript. Look for identical tool calls, changing arguments that make no progress, retries that repeat a failed action, or a result or error that the next step appears to ignore. Check whether the orchestrator has a stopping condition and whether the run ends by reaching a maximum step budget.
For an action-oriented agent, a regression case can check whether expected tool calls occur and whether a repeated sequence or lack of progress appears. LangChain’s evaluation types describes reference tool calls and heuristic evaluators for ReAct agents; adapting that pattern can help detect a known loop, but it is not a universal loop detector.
If the agent stalls or takes too long
Find the latest completed event and the next event that has not completed. Check tool and network timeouts, queue delays, long-running external calls, blocked approval or handoff flows, and whether streaming events are still arriving. Timestamps and event progression help distinguish a truly hung operation from one that is slow but still advancing.
If the last visible step is an API request, correlate it with the request’s error information, processing time, and rate-limit headers. Those details can help narrow whether the issue lies in application orchestration or the API request path, although they do not identify the cause on their own.
If the agent returns the wrong result
Follow the evidence from start to finish: was retrieved material relevant, did the tool return the expected data, did the agent choose the appropriate tool, and did the final response preserve the evidence? Compare the result with a reference answer or task-specific criteria. For agents that take actions, compare the actual tool sequence with the sequence expected for that task.
LangChain’s evaluation documentation covers offline benchmark datasets, regression tests, and backtesting production examples against newer versions. Those methods help test whether a change fixes a known failure without introducing a regression elsewhere.
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Correlate the agent trace with API evidence
Keep a stable agent run identifier and correlate it with API request identifiers, errors, headers, and timing. OpenAI’s API debugging guidance recommends logging the server-generated x-request-id in production and describes the client-supplied X-Client-Request-Id for correlation when a network failure or timeout prevents receipt of a server ID. The client ID must be unique per request, use ASCII characters, and be no more than 512 characters, according to that reference.
Useful run records include parent and child step relationships, model request and response metadata, tool inputs and results, timestamps and durations, retry count, terminal reason, and final output. OpenAI’s API reference also recommends inspecting error codes and response headers for request identity and rate-limit details. Keep identifiers linked to the corresponding agent trace so an API event is not mistaken for a separate run.
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Change one cause, rerun the same case
Once the first divergence is identified, choose one plausible cause to test while holding the failing input and other conditions steady. Candidates include instruction wording, tool descriptions or schemas, state handling, retry and termination logic, external-service behavior, model configuration, and data freshness. They are hypotheses, not conclusions implied by a trace.
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After each change, rerun the same case and compare the relevant event sequence and output. If the behavior changes, verify that the proposed cause explains the difference; if it does not, return to the trace rather than layering on additional changes.
Model behavior can also vary between snapshots. OpenAI’s backwards-compatibility guidance recommends pinned model versions for more consistent prompting behavior and application evaluations. Pinning helps with reproducibility; it does not replace testing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Turn confirmed failures into evaluations
Build a small, curated evaluation set from real failures and representative normal cases. Choose a check that matches the task:
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- Required structure or actions: use deterministic or rule-based checks for output structure and required tool calls.
- Answer quality: compare with reference outputs or task-specific semantic criteria when exact matching is unsuitable.
- Action correctness: compare actual tool calls with expected calls or a reference sequence.
Compare changes with a baseline and rerun the relevant cases whenever a change is likely to affect behavior. LangChain’s evaluation types describes offline benchmarking, unit and regression tests, backtesting, and pairwise evaluation. A test set should grow from confirmed failures rather than trying to encode every possible behavior at once.
Monitor production runs and compare observability options
Review production traces for unusually long runs, repeated tool calls, errors, and quality regressions. Feed confirmed failures back into the offline evaluation set. LangChain documents online evaluators that can be filtered by user feedback, specific tool calls, or trace metadata, and sampled to manage evaluation cost. Its online evaluator setup guidance also notes that matching traces are upgraded to extended data retention, which affects trace pricing; check current plan and retention settings before enabling them.
Whether you use framework-native tracing, a vendor platform, or internal logging, compare these practical requirements:
- Compatibility: does it work with your framework and runtime?
- Trace detail: can you inspect parent and child steps, tool inputs and results, and errors?
- Findability: can you filter by run metadata and tool?
- Privacy and operations: do retention, access control, data residency, cost, and maintenance fit your requirements?
- Evaluation support: can you maintain offline datasets, compare regressions, and monitor production with manageable evaluator sampling?
The cited LangChain documentation describes filtering, online evaluation, sampling, and retention implications; it is not a cross-vendor comparison. Assess candidate tools against your own compatibility, privacy, and operational needs.
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