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To replay an AI agent run, first inspect the trace to find the earliest step that behaved unexpectedly. Then decide whether you need to review recorded history, resume from saved workflow state, or run the application again with controlled inputs. Those are different operations: a trace records what happened; a checkpoint stores workflow state; and a fresh rerun may produce different results.
What “replay” means when debugging an agent
The phrase replay an AI agent run can describe several different tasks. Be explicit about which one you mean before changing anything:
- Trace inspection: Read the stored record of an execution. LangChain describes a trace as an ordered collection of runs; a thread can group traces across turns in a multi-turn interaction. Depending on the instrumentation, a trace may include the request, retrieved context, tool arguments and responses, intermediate steps, and final response. LangSmith’s trace concepts explain the trace and thread model.
- Checkpoint time travel or resume: Examine or restore persisted workflow state in a framework that supports checkpointing. In LangGraph, a checkpointer enables time-travel debugging and replay of prior graph executions. The exact API and behavior depend on the framework, version, and checkpoint backend. See the LangGraph replay documentation.
- Fresh rerun: Execute the application again with captured inputs. This is a reproduction attempt, not proof of an identical execution: model responses, live tools, external services, and runtime conditions may have changed.
- Recorded-call replay: A custom test harness can replace live tool calls with saved responses. This is application-specific; document which calls are stubbed and which remain live.
Observability and execution recovery are related but not interchangeable. LangSmith, for example, describes agent tracing and monitoring, along with framework and OpenTelemetry support; a trace viewer should not be assumed to re-execute an agent. LangSmith’s overview describes its observability features.
Find the first unexpected step
Start at the beginning of the execution tree and move forward. The first divergence from the expected workflow is usually more informative than the final error: a bad retrieval result or tool response can cause several later steps to fail.
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- Preserve the failure record. Save the trace or run ID and timestamp, plus the agent/code revision, model and configuration identifiers, and environment details available to your team. Avoid exposing secrets or collecting personal data that is not needed to debug the problem.
- Follow the execution tree. Read the root run and its nested model, retriever, and tool runs in order. Identify the first output, decision, or transition that does not match the intended workflow.
- Inspect inputs and outputs at that point. Compare the original request, retrieved documents and their versions, tool arguments and responses, and the state passed between steps. Check parent-child run context too: it can show where a value came from and which step consumed it.
- Trace downstream effects. Once you find a likely divergence, check whether later errors follow from it. For example, an incorrect tool result may explain a confident but wrong final answer without the final model call being the original cause.
LangSmith’s production-data evaluation documentation describes evaluating and backtesting against production examples. That can help turn a useful failure case into a regression check if your team’s evaluation workflow supports it; it is not required for basic trace debugging.
Choose the right way to reproduce the failure
| Method | What it does | Use it when | What it does not guarantee |
|---|---|---|---|
| Trace inspection | Shows recorded events and run relationships. | You need to understand what happened in the historical execution. | It does not, by itself, execute the agent again. |
| Checkpoint time travel or resume | Examines or restores persisted workflow state where the framework supports it. | You need to inspect or continue a checkpointed graph from a selected point. | Behavior and APIs vary by framework and implementation; resuming is not necessarily a byte-for-byte repeat. |
| Fresh rerun | Executes the application again with saved inputs. | You need to test whether a suspected fix changes the outcome. | Live model, tool, external-service, or runtime behavior may differ from the original. |
| Recorded-call replay | Uses saved responses in place of selected live calls in a custom harness. | You need to isolate an agent step from changing dependencies. | It only controls calls your harness actually records and substitutes. |
Using a checkpointed workflow
In LangGraph, checkpointing is the relevant mechanism for saved graph state, not the trace itself. Its documentation describes checkpointers as enabling time travel to review or debug specific steps. Follow the documentation for the framework version and persistence setup your application uses rather than assuming a universal replay command.
One design trade-off matters during recovery: resuming from a checkpoint can repeat work in the node where execution stopped. Smaller node boundaries can make it easier to inspect progress and limit the work repeated after a failure, but they also affect workflow design and should be chosen deliberately.
Making a rerun more controlled
For a fresh rerun, preserve the inputs that can reasonably be captured: user request, retrieved content and versions, state passed between steps, tool arguments and results, model/configuration identifiers, and relevant runtime settings. Capture only what your privacy and security requirements permit. If some calls use saved responses while others are live, label that distinction in the test record. Even with the same inputs, a rerun should not be described as an exact reproduction unless the relevant model and external-service behavior is also controlled.
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Change one cause and compare the traces
After identifying a plausible source of divergence, change one thing at a time—for example, a retrieval filter, tool schema, prompt, or routing condition. Compare the new execution with the original at the step where the old run first diverged, then check whether downstream behavior changes as expected. If your team maintains evaluations, save the captured failure as a regression example so future changes can be checked against it.
If similar failures recur, group them by the component implicated in the trace: tool, graph node, model/configuration, or retrieval source. Monitoring failure rates and latency for that component can help distinguish a one-off bad result from an operational pattern. Choose observability tooling based on the frameworks you use, the trace fields you need, deployment and data-handling requirements, retention, search, evaluation support, and cost; a product such as LangSmith is one option, not a prerequisite.
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When a trace cannot be uploaded
LangChain Support documents an offline workaround for traces captured as failed traces by its SDK. The September 8, 2026 article describes setting LANGSMITH_FAILED_TRACES_DIR and optionally LANGSMITH_FAILED_TRACES_MAX_MB so the SDK stores failed trace JSON for later posting to the endpoint. It is specifically a workaround for traces captured through that SDK mechanism, not a general-purpose trace import facility. Verify the current SDK documentation before relying on these environment variables, and do not delete a saved file until its POST succeeds. LangChain Support’s failed-trace recovery instructions provide the stated scope and procedure.
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