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To test and replay an AI agent locally, save a representative input and the state needed to start the run, replay it using one clearly defined state strategy, inspect the full trace, and assert on the answer, tool behavior, and resulting state. Keep recurring failures as dataset cases and rerun them after changes. Replay-ready history helps you continue or reproduce a conversation’s inputs; it does not guarantee identical model outputs or external-service behavior.
What an agent run includes
An agent run is more than the text it returns. It can include model calls, tool execution, handoffs between agents, and eventual completion. A test that checks only the final response can miss a wrong tool choice, bad arguments, an unexpected handoff, or an incorrect state change. OpenAI describes this full workflow in its guide to running agents.
For a useful test, decide what success means at three levels: the final answer, the path the agent took, and any state or artifacts the run was expected to change. LangChain likewise treats output, trajectory, and resulting state as distinct evaluation concerns in its run, trace, and thread evaluation overview.
How do I replay an agent run locally?
Capture a representative case
Save the user input and the state required to begin the run. Record the behavior you expect, including important tool calls or state changes. If you are investigating a failure, retain the relevant trace and note which agent, prompt, and tool implementation produced it. This makes later comparisons meaningful when any of those components change.
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Choose one conversation-state strategy
In the OpenAI Agents SDK, a result exposes application-held history that can be used as replay-ready conversation input: use history in TypeScript or to_input_list() in Python. OpenAI distinguishes these local history surfaces from server-managed continuation options, such as response IDs. See the results and state guide and the running agents guide for the relevant state patterns.
Pick a single continuation strategy for a conversation. If you combine replayed local history with server-managed conversation state, reconcile what each contains; otherwise, the same context can be included twice. Local history preserves conversation input, but it does not freeze model sampling, external API responses, or side effects. The cited documentation does not promise bit-for-bit deterministic replay across frameworks or services.
Control external effects
For a test that could update real data or trigger an external action, isolate or stub that boundary where practical. Feed the test controlled inputs, then assert that the intended state change occurred without allowing an unintended real-world action. There is no universal replay mechanism for external side effects established across agent frameworks, so the boundary depends on your application.
How do I test an agent workflow?
Inspect the trace from start to finish
OpenAI defines a trace as “the end-to-end record of model calls, tool calls, guardrails, and handoffs for one run.” Follow those events alongside the final result and find the first point where behavior diverged from the intended path. OpenAI recommends trace inspection and trace grading as a way to identify workflow-level issues; see its agent evaluations guide.
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Assert at the level of the behavior
- One step: Check a particular tool choice, its arguments, or an intermediate output when isolating a suspected failure.
- One complete turn: Check the answer, whether the trajectory was acceptable, and whether expected state or artifacts changed.
- A multi-turn thread: Check that behavior remains appropriate as conversation state accumulates across turns.
Use direct assertions for concrete expectations such as a required tool call or state transition. For qualities that are harder to express as exact values, such as semantic correctness, judge-based scoring can supplement those checks. LangChain describes evaluation approaches and their trade-offs in its evaluation types documentation.
How can I catch agent regressions?
Trace inspection helps diagnose an individual run. To catch repeat failures, turn representative cases into a maintained dataset and run it after changes to code, prompts, routing, or models. Include expected answers where appropriate and expected tool behavior for cases where the path matters. Compare outcomes across runs rather than relying on memory of how one example behaved. OpenAI describes datasets and evaluation runs as the repeatability step after trace-level debugging in its agent evaluations guide; LangChain’s evaluation types documentation discusses benchmark cases with reference answers or tool calls.
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Choosing a local or hosted evaluation approach
| Decision | Options | What to consider |
|---|---|---|
| State ownership | Application-held history or session; server-managed conversation or response continuation | Choose one continuation strategy for each conversation and reconcile state explicitly if both are involved. OpenAI running agents guide |
| Test scope | One step or run; complete agent-turn trace; multi-turn thread | Match the scope to the failure: a tool argument bug may need a step-level check, while a state-carryover issue may need a thread. LangChain run, trace, and thread evaluation overview |
| Evaluation method | Direct assertions; judge-based scoring; or both | Use direct assertions for specific tool and state expectations; consider judge-based scoring for qualities such as semantic correctness. LangChain evaluation types |
| Where evaluation runs | Local scripts; hosted evaluation or observability product | Choose based on workflow and operational needs. Confirm the selected tool’s current data handling and configuration; the cited sources do not establish every product’s privacy or deployment details. |
LangChain reported that 89% of surveyed organizations had implemented observability, 52% ran offline evaluations on test sets, and 37% ran online evaluations. These are figures from LangChain’s article of June 23, 2026, summarizing its State of Agent Engineering survey—not universal industry measurements. See the survey reporting.
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