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If an AI agent gave the wrong answer, a clean-looking chat transcript may not show why. Inspect the recorded tool trace: the run inputs, model output, tool selected, arguments sent, result returned, and order of operations. Replaying that record can make a failure easier to investigate, but it does not guarantee the model will behave identically again.
Why the tool trace matters more than the chat alone
A transcript captures the user-facing exchange. It may omit the execution steps between a request and the assistant’s final answer: which tool the model chose, what arguments it supplied, what the tool returned, and whether the sequence was appropriate.
That missing context matters when an answer is wrong. The model may have selected the wrong tool, supplied a malformed argument, misunderstood a valid result, or acted on stale or incomplete information. Looking only at the final reply can make these different failures look alike.
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What to capture for a useful investigation
Preserve enough context to follow the run from its inputs to its outcome. Fiddler describes traces that capture prompts, model calls, tool invocations, and retrieval as spans. In practice, check that the record makes the steps and their relationships clear. Fiddler’s overview of LLM tracing
- Run inputs: the relevant user request and context supplied to the model.
- Model outputs: the response at each decision point, including a tool call when the model requests one.
- Tool identity and arguments: which tool ran and the exact arguments it received.
- Tool result: what the tool returned, including errors where recorded.
- Step order: the sequence of model and tool events, so you can see what information was available at each point.
- Run metadata: include token or cost information if your tracing system records it and it is relevant to the investigation.
Without arguments, results, and ordering, a trace can show that a tool was involved without showing whether the agent used it correctly. Keep the record focused on the context needed to explain the outcome, especially when prompts or tool results contain sensitive data.
How to use replay to debug a recorded run
Treat a recorded failure as a debugging case. First inspect the trace as captured; then, if your system supports it, use replay to examine or retest the sequence. Compare the original events with the new run rather than assuming replay will reproduce every response exactly.
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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 match- Locate the failing run. Start with the final answer or observed side effect that was wrong, then find the associated trace.
- Follow events in order. Check the inputs, each model decision, tool name and arguments, returned result, and the model’s response to that result.
- Identify the earliest divergence. Determine whether the failure began with the model’s choice, the supplied arguments, the tool’s result, or a later interpretation.
- Retest deliberately. If replay is available, use it to inspect or retest the case and compare outputs and tool events with the original record.
- Record the diagnosis. Note what changed or failed, and retain only the trace data needed for debugging under your organization’s handling rules.
Replay is a debugging aid, not a determinism guarantee. The same prompt can produce different outputs across runs, so a new response that differs from the original does not by itself prove the recorded trace was inaccurate or that the failure is fixed. Fiddler’s discussion of tracing and run variation
Replay and trace evaluation answer different questions
Re-executing a run asks what happens when the model and tools run again. Evaluating a supplied trace asks whether the recorded task, steps, and claimed result meet an evaluator’s criteria; that evaluation need not execute the tools again.
Jev describes its evaluator as judging the task, trace, and claimed result supplied to it, while the caller’s harness is responsible for execution and logging. So if your question is “Did the agent take the right steps in this recorded run?”, an evaluator may assess the evidence already captured. If your question is “What happens if I run this again?”, that is replay or re-execution. Jev’s description of trace evaluation
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Set safe replay rules for tools with side effects
Some tools only retrieve information; others can change external state. Re-running a tool that sends a message, updates a record, or performs another action could repeat that effect. Before allowing replay, define the tool’s contract and decide what the replay system is permitted to do.
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For each tool, document the questions that determine whether it is safe to replay:
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- Preconditions: What must be true before the tool can run?
- Permitted use: Is this tool allowed for the task and the current run?
- Argument rules: Which arguments are valid, and how should they be checked?
- Result semantics: What does success, failure, or a partial result mean?
- Side effects and idempotency: What external state can change, and would repeating the call duplicate an action?
- Evidence and replay policy: What should be logged, and should replay use a mock, a safe test environment, or a live tool?
These are design questions, not guarantees that a particular tracing system will prevent unsafe actions. The replay policy should match the consequences of each tool call. Agent tool-contract considerations
Protect prompts and outputs in stored traces
Trace data can include raw prompts and model outputs. Those records may contain information that should not be broadly visible or retained indefinitely, so decide how traces are governed before making them routine debugging artifacts.
- Limit access to people who need the trace for debugging or evaluation.
- Set retention rules that fit the sensitivity and purpose of the recorded data.
- Review whether prompts, outputs, arguments, or tool results need redaction before storage or sharing.
- Make sure replay and evaluation workflows follow the same handling rules as the original run.
Fiddler specifically warns that trace data can contain raw prompts and outputs, making data governance relevant from the start. Fiddler’s tracing overview
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