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A clean-looking chat transcript can hide the step that caused an agent run to fail: a tool call with the wrong arguments, an unexpected result, or an operation that happened in the wrong order. To debug the outcome, inspect the recorded execution path—not just what the user and assistant said.
What a tool trace reveals that chat does not
A transcript preserves the conversational surface. A trace can show the sequence of events behind it: the inputs to model calls, the model’s outputs, tool calls and their arguments, the results returned by tools, and the order in which those steps occurred. Depending on the system, it may also record token and cost information. OpenLegion describes this kind of reconstruction as trace replay; it is a description of that capability, not a universal definition for every tracing product. OpenLegion’s explanation of trace replay
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Tracing can also represent work such as retrieval and model calls as spans. Fiddler describes tracing prompts, model calls, tool invocations, and retrieval in this way. Fiddler’s overview of LLM tracing
The Tool Desk
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What to preserve for a useful investigation
Capture enough context to understand both the decision and its consequences. At minimum, inspect the following for the run:
- Relevant inputs: the user request and any context passed into the model or retrieved during the run.
- Model outputs: the content that led to a tool call, as well as the final response.
- Tool identity and arguments: which tool was invoked and the exact arguments sent to it.
- Tool results: the response, error, or other result returned to the agent.
- Step order: the sequence of model calls, retrieval, tool calls, and results.
Without arguments and results, a trace may show that a tool was involved but not whether it received the right request or returned usable evidence. Without ordering, it can be difficult to tell whether the agent acted on current information or on an earlier result.
How replay helps—and what it cannot promise
Replay lets an engineer use a recorded run as a debugging case: inspect its execution sequence and, where the system supports it, retest the case to investigate a failure. This can make it easier to locate the step where behavior diverged from expectations. It does not mean every model response will be reproduced exactly. The same prompt can produce different outputs across runs, so a replay is not a guarantee of deterministic behavior. Fiddler’s discussion of trace variability
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhen comparing a replay with the original, separate what is held constant from what can change. The stored inputs and tool results may be available for inspection, while a new execution can involve a fresh model response or renewed tool activity. The precise behavior depends on the replay system and its configuration; do not assume that “replay” means either a purely historical playback or a full re-execution unless the product documents that distinction.
Replay and trace evaluation are different operations
Replaying execution and evaluating a trace answer different questions. Replay concerns inspecting or retesting a run’s execution. Evaluation can judge a supplied record without carrying out the tools again.
Jev describes its evaluator as judging the task, trace, and claimed result supplied to it; the caller’s harness is responsible for execution and logging. That is an example of trace evaluation, not evidence that the evaluator itself repeats the tool calls. Jev’s evaluator description
This distinction matters when interpreting a result: a judgment about a recorded trace is not proof that an external action was newly performed, and a successful replay does not by itself establish that the original trace met the task’s requirements.
Set replay rules before tools can change state
Some tool calls only read information; others can send messages, change records, or trigger actions outside the agent. A replay policy should account for those consequences before anyone retests a run. A tool-call contract can make the relevant questions explicit. Fiddler’s tracing overview
- Preconditions: What must be true before the call is allowed?
- Permitted tools: Which tools may be used for this task?
- Argument rules: Which values are valid, required, or prohibited?
- Result semantics: What does success, failure, or a partial result mean?
- Side effects and idempotency: Could repeating the call create another external change, and is repeating it safe?
- Evidence: What record is needed to verify the action and its outcome?
- Replay policy: Should a retest suppress side effects, use a safe test environment, or require explicit approval?
Do not treat a potentially state-changing call as safe to repeat merely because it appears in a trace. Whether it can be replayed safely depends on the tool’s behavior and the safeguards around it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect prompts and outputs in stored traces
Trace data can contain raw prompts and model outputs, so decisions about retention and access belong in the design of tracing—not as an afterthought. Fiddler flags data governance as relevant for this reason. Fiddler’s tracing overview
Before retaining traces, decide who may inspect them, how long they are kept, and whether sensitive content should be removed or masked. Preserve the execution details needed to investigate behavior while limiting exposure of information that does not need to be available to every person or system handling the trace.
Questions to ask about a tracing system
When assessing whether a system supports your debugging needs, focus on its documented behavior rather than assuming that all trace or replay features work alike:
- Does the trace include the relevant inputs, model outputs, tool names, arguments, results, and step order?
- Can you inspect a recorded run, retest it, or both—and what exactly happens during each operation?
- Can replay prevent or safely contain external side effects?
- What controls govern access to and retention of sensitive prompts and outputs?
These questions help you determine whether a trace can explain an outcome and whether replay is appropriate for the tools involved. They do not imply that one product is superior to another.
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