To test and replay an AI agent locally, save a representative input and starting state, rerun it using one clearly defined state strategy, inspect the full run trace, and add checks for the answer, tool behavior, and resulting state. Keep recurring failures as test cases and rerun them after changes. Replay-ready conversation history helps you continue a run locally; it does not guarantee identical model outputs or external-service behavior.
What should a local agent test verify?
An agent run is more than the response a user sees. It can include multiple model calls, tool execution, guardrails, handoffs between agents, and a final completion. A useful test checks the parts of that sequence that matter to the task, not just whether the final text looks plausible. OpenAI describes the agent loop in its guide to running agents.
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- Output: Did the agent give an acceptable answer?
- Trajectory: Did it choose the right tool, pass suitable arguments, and follow an acceptable path?
- State: Did the expected application data or artifact change, and did unrelated state remain untouched?
These are distinct evaluation concerns: a correct-looking answer can conceal an incorrect tool call or an unwanted state change. LangChain’s overview of run, trace, and thread evaluation likewise separates output, path, and resulting state.
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First choose where the conversation state lives. In the OpenAI Agents SDK, the result exposes replay-ready history: use history in TypeScript or to_input_list() in Python as conversation input. These are application-held history surfaces; they are distinct from response IDs and other server-managed continuation options. See OpenAI’s results and state guide.
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- Capture the case: Save the user input, the application state needed to start the run, and the behavior you expect. For a known failure, retain the relevant trace and record the agent, prompt, and tool implementation versions.
- Choose a continuation strategy: Use local history or server-managed conversation/response state deliberately. If your application uses both, reconcile them rather than feeding the same context through both paths; otherwise, context may be duplicated.
- Rerun under controlled conditions: Keep the input and relevant starting state fixed. Stub or isolate external services and side-effecting tools when a test could change real data or trigger an outside action.
- Compare the run with the expected behavior: Inspect its steps and check the output, tool behavior, and state changes that matter to the case.
Replay here means reusing saved inputs and conversation history to run the scenario again—not guaranteeing a bit-for-bit repeat. The cited documentation describes replay-ready state surfaces, but does not promise identical model sampling, external API responses, or side effects across frameworks and services. Make the boundary explicit in your own test: decide which services are mocked, which state is restored, and which actions are allowed to execute.
How do I find where a run went wrong?
Start with one representative trace and find the first point where the run diverged from the intended behavior. OpenAI defines a trace as an end-to-end record of model calls, tool calls, guardrails, and handoffs for one run, and recommends trace grading to help identify workflow-level problems. Its agent evaluations guide covers trace inspection and evaluation.
- Follow the sequence: Check each model call, tool call and its arguments, guardrail result, handoff, and final result.
- Locate the earliest meaningful divergence: A bad final answer may result from an earlier routing decision, missing context, unsuitable tool arguments, or an unexpected state change. Test the earliest broken step rather than patching only the last response.
- Separate one-step checks from whole-run checks: When isolating a particular decision, assert the tool choice, arguments, or intermediate output. For the full turn, also check whether the trajectory was acceptable and the expected state or artifacts changed.
A trace is useful for diagnosis; a repeatable test is what helps establish whether a fix holds on the cases you care about.
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When a failure recurs or a scenario is important, turn it into a maintained dataset case. Include a representative input and the expected result or behavior, including tool calls where relevant. OpenAI recommends moving from trace-level debugging to datasets and repeatable evaluation runs; LangSmith’s evaluation types describes benchmark cases using reference answers or tool calls.
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- Keep cases specific: Record what a passing run must answer, do, or change. Avoid expectations so vague that a regression can pass unnoticed.
- Rerun after changes: Run the dataset after edits to code, prompts, routing, or model selection, then compare results with the prior run.
- Use the right evaluator: Use direct assertions for concrete requirements such as a tool call, argument, or state transition. Consider judge-based scoring for qualities such as semantic correctness that are harder to capture with a simple exact match.
- Control risky boundaries: Isolate external actions and assert on intended state changes so a regression test does not accidentally affect production data.
For a small workflow, local scripts and assertions may be enough. A hosted evaluation or observability product can help manage larger datasets and traces, but check the selected product’s current data-handling and configuration details before sending traces or inputs to it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which replay and evaluation approach fits the job?
| Decision | Options | Use it to decide |
|---|---|---|
| State ownership | Application-held history/session or server-managed conversation/response continuation | Where the starting context comes from and how to avoid duplicating it. |
| Test scope | One step or run, a complete agent-turn trace, or a multi-turn thread | Whether you are isolating a decision or validating a longer interaction. |
| Evaluation method | Direct assertions or judge-based scoring | Whether behavior has a crisp expected value or needs a semantic quality assessment. |
| Operational handling | Local scripts or hosted evaluation/observability tooling | How you run and review tests; verify current data-handling details with the chosen tool. |
LangChain reported in its June 23, 2026 article summarizing its State of Agent Engineering survey that 89% of surveyed organizations had implemented observability, 52% ran offline evaluations on test sets, and 37% ran online evaluations. These are figures from that survey reporting, not universal measurements of all organizations.
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