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Debugging a long-running AI agent requires more than checking whether its final answer is correct. Treat the agent as a stochastic workflow, record its complete trajectory, and test the smallest decision, the full task, and the state carried across turns. Then turn every real incident into a versioned regression scenario, replay it under identical conditions, and find the first decision that made recovery impossible.
This approach exposes failures caused by bad plans, invalid tool calls, misunderstood tool output, lost checkpoints, expired approvals, latency limits, and context growth. It also lets you verify that a fix improves the original case without quietly damaging other scenarios.
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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsWhy do agents pass short tests but fail on long-running tasks?
Short tests usually inspect one prompt and one response. A long-running agent accumulates model decisions, tool calls, retrieved documents, retries, waiting periods, approvals, and state changes. A small mistake early in that trajectory can make the final result wrong even when the last response appears coherent.
Errors compound across the trajectory
An agent may choose an unsuitable plan, call a tool with an invalid argument, misread the response, and then continue confidently. The final failure is only the visible symptom; the first unrecoverable decision may be many steps earlier. Microsoft Research’s AgentRx work describes this as a diagnostic problem: traditional measures such as “Did the task finish?” do not identify enough about what happened in between.
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Stochastic behavior hides intermittent defects
Sampling settings, model updates, retrieval order, timing, and tool availability can change a run. One successful execution therefore does not establish reliability. Repeat the same scenario and report results per scenario, not only as one aggregate score.
Interruptions expose state-management bugs
Waiting for a human, retrying a service, restarting a worker, or reaching a context limit can expose missing or inconsistent state. An agent that works in one uninterrupted process may lose progress or repeat side effects after a restart.
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What behavior contract should every test scenario have?
Write a contract before running the scenario. It defines what the agent is allowed to do and what counts as success, so a grader can distinguish a safe alternative from an accidental pass.
- Intent and inputs: state the user goal, required data, optional data, and ambiguity the agent must resolve.
- Allowed tools: list each callable tool, permitted arguments, authentication scope, and any tools that must not be used.
- Safety constraints: specify confirmation requirements, privacy restrictions, spending limits, write permissions, and escalation rules.
- State transitions: describe valid states such as planned, awaiting approval, executing, partially complete, completed, or failed.
- Success criteria: define observable outputs, side effects, data integrity, and whether the user’s intent was met.
- Operating limits: set maximum steps, tool calls, latency, context size, retries, and cost appropriate to the workflow.
- Recovery rules: state what should happen when a tool is unavailable, an approval expires, or execution is interrupted.
Make each scenario independently passable by a human reviewer. Avoid tests that depend on an unstated sequence of hidden assumptions; otherwise the grader will measure the test author’s expectations rather than the agent’s behavior.
What should a complete agent trace record?
A final answer is not sufficient evidence. Capture a structured trace for every execution so you can reconstruct the exact trajectory and compare runs safely.
| Trace element | What to record | Why it matters |
|---|---|---|
| Model calls | Prompt or message identifiers, model and version, sampling settings, response, token counts, and latency | Shows which model decision introduced a change and exposes context or cost growth |
| Retrieval | Query, filters, returned document identifiers, ranking, snippets, and retrieval latency | Separates missing evidence from reasoning or tool-use errors |
| Tool requests | Tool name, validated arguments, caller step, authorization context, and timestamp | Reveals invalid invocation, unsafe scope, and duplicate side effects |
| Tool responses | Raw or redacted response, status, schema-validation result, and elapsed time | Shows whether the agent misunderstood a valid response or received a failure |
| Control decisions | Plan updates, routing, retry choice, stop condition, escalation, and state transition | Identifies the first decision that made the trajectory unrecoverable |
| Errors and limits | Exceptions, timeouts, rate limits, guardrail events, context size, step count, and cumulative cost | Connects a failure to an operating boundary instead of blaming the model generally |
| Versions | Task, prompt, tool schema, model, retrieval index, evaluator, code, dataset, and threshold versions | Makes a comparison reproducible after a deployment or configuration change |
Use correlation and span identifiers so a tool response can be traced back to the model call that requested it. Redact secrets and minimize sensitive content, but preserve enough structure to reproduce the decision. Databricks describes tracing as the foundation for agent observability and quality; in practice, that means traces must be useful both during an incident and during offline evaluation.
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How do I build a regression dataset from real failures?
Start with incidents that users or reviewers already found, then add the manual checks developers perform repeatedly. Anthropic recommends 20–50 simple tasks drawn from real failures as a practical initial evaluation set. That is a starting point, not a universal sample size.
- Collect the complete incident trace. Preserve the original task, relevant context, tool inputs and outputs, timing, versions, and user or reviewer feedback.
- Reduce it to a deterministic scenario. Replace unavailable dependencies with recorded responses or controlled simulators, while keeping the condition that triggered the defect.
- Write an explicit grader. Check required state transitions, tool arguments, safety rules, side effects, and final user-visible outcome. Use more than a text-similarity check when the task changes external state.
- Make the case independently passable. A reviewer should be able to judge it without knowing the implementation or the expected chain of thought.
- Label the failure mechanism. Record whether it involved plan adherence, invented information, invalid invocation, tool-output misinterpretation, intent–plan misalignment, underspecified or unsupported intent, a guardrail trigger, or system failure.
- Add a successful counterpart. Keep a representative passing example with similar inputs so a fix is not optimized only for failures.
- Version the case and grader. Changing the prompt, tool schema, evaluator, or threshold creates a new version and must be visible in result history.
- Run repeated trials. Stochastic scenarios need multiple attempts under the same configuration. Store every trial, not just the pass rate.
Keep scenario-level results alongside the aggregate. A higher overall score can conceal a regression in a high-risk workflow if many easy cases dominate the average.
Which evaluation level should I use: run, trace, or thread?
Match the test granularity to the failure. Teams usually need all three levels rather than choosing one.
| Level | What it tests | Best use | Typical assertion |
|---|---|---|---|
| Run-level | One local decision or tool call | Fast checks for known failure modes and schema boundaries | “The request uses a valid account identifier and does not exceed the allowed amount.” |
| Trace-level | The complete task in one turn or execution | Core workflow regression and end-to-end safety | “The task reaches the required state without an unapproved side effect.” |
| Thread-level | State carried across multiple turns or sessions | Approvals, corrections, follow-up requests, and resumptions | “After the user changes one requirement, prior confirmed facts remain intact.” |
Use full-turn tests for core workflows, single-step tests for localized defects, and thread tests whenever state persists beyond one execution. A run-level pass cannot prove that a later turn will interpret the saved state correctly.
How do I find the first unrecoverable decision?
Read the trace from the beginning and mark the earliest point at which the agent violated its contract or lost the information needed for recovery. Do not start with the final error message.
A practical first-critical-step method
- Replay the trace in chronological order, including retrieved evidence and tool responses.
- At each decision, compare the chosen action with the behavior contract and the state available at that moment.
- Mark the first invalid plan, unsupported assertion, malformed request, misread response, unsafe action, or lost state.
- Check whether a later step could still have corrected the error. If not, classify that point as the critical step.
- Separate the initiating defect from downstream symptoms such as repeated retries, a timeout, or a misleading final explanation.
AgentRx’s taxonomy is useful for consistent labels: plan-adherence failure, invention of new information, invalid invocation, misinterpretation of tool output, intent–plan misalignment, under-specified intent, unsupported intent, guardrail trigger, and system failure. Microsoft Research reported results from 115 manually annotated failed trajectories; its approach improved failure localization by 23.6% and root-cause attribution by 22.9% over prompting baselines in that evaluation. Those figures describe that benchmark, not a universal improvement for every agent.
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How can I reproduce a production-only tool-use failure?
Replay the failing case with recorded or simulated dependencies before changing the agent. Deterministic replay isolates the agent from a changing API, while controlled fault injection lets you test the condition that production exposed.
- Freeze the comparison. Use the same scenario, task and prompt versions, model configuration, tool schemas, evaluator version, and thresholds for the baseline and candidate.
- Reconstruct dependency behavior. Replay recorded responses or build a simulator that returns the same status codes, delays, malformed payloads, and partial results.
- Inject one fault at a time. Try an unavailable tool, delayed response, expired credential, duplicate delivery, truncated output, rate limit, and contradictory data separately before combining them.
- Compare trajectories, not just answers. Diff plans, arguments, retries, state transitions, latency, token use, and side effects.
- Repeat stochastic trials. Report the distribution by scenario and distinguish a genuine improvement from random variation.
- Promote the case to regression. Keep the original trace, the minimized reproduction, and the expected grader result together.
Shadow or production scoring can reveal behavior that offline fixtures miss, but it must respect access controls and data-minimization rules. Never replay a write-capable tool against production merely to reproduce a defect; use a sandbox or a side-effect-free simulator.
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How should I test waits, retries, approvals, and restarts?
If progress must survive an interruption, retries alone are not enough. Use durable execution with persisted checkpoints and resumable state. The OpenAI Agents SDK documents integrations for Dapr, Temporal, Restate, and DBOS; these systems are designed to resume long-running workflows after waits or process failures.
Test the normal path and the failure path
- Pause before a human approval, then resume with the approval accepted.
- Let the approval expire and verify a safe, explicit recovery state.
- Kill the worker after a tool commits a side effect but before the result is recorded.
- Deliver the same retry twice and verify idempotency or duplicate detection.
- Restart with a missing or corrupted checkpoint and confirm that the workflow stops safely rather than guessing.
- Make a required tool unavailable and check that the agent preserves context, reports the limitation, and follows the escalation rule.
- Resume after a context-window boundary and verify that essential state is restored without copying untrusted text into instructions.
For each interruption test, assert both the persisted state and the external side effects. A workflow that resumes at the right step but repeats a payment, deletion, or message is still incorrect.
How do I turn traces into a continuous quality loop?
A useful quality loop is operational, not a one-time benchmark: trace executions, identify an issue, collect reviewer or user feedback, curate representative failures and successes, write graders, fix the agent, rerun the evaluation, and monitor production traces for recurrence.
Tracing and evaluation platforms such as MLflow or LangSmith can help organize runs, datasets, graders, and comparisons; choose tools according to your deployment, retention, and access requirements rather than assuming a platform replaces instrumentation in the agent itself. The source of truth should remain versioned records of the task, prompt, tools, model, dataset, evaluator, and threshold.
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Use complementary graders
- Deterministic assertions for schemas, required fields, state transitions, tool permissions, limits, and side effects.
- Model-based graders for nuanced criteria such as relevance or whether a response addressed an ambiguous request, with a fixed rubric and periodic calibration.
- Human review for safety-sensitive, novel, or disputed cases, feeding the decision back into the dataset.
Store grader explanations and disagreement rates. A model-based score that changes when the evaluator model changes is not directly comparable unless the evaluator version is recorded.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which operating limits belong in a long-running test plan?
Exercise the boundaries explicitly; otherwise a test suite can pass while the production workflow fails under load or duration.
| Limit | Boundary test | What to verify |
|---|---|---|
| Maximum steps | Provide a task requiring the allowed number, then one requiring one more | Clean completion at the limit and a controlled stop beyond it |
| Tool-call count | Return slow or partial responses that tempt repeated calls | Backoff, deduplication, and an intelligible escalation |
| Latency | Delay a dependency near the timeout and beyond it | Cancellation, checkpoint persistence, and no orphaned side effect |
| Context size | Gradually add history and retrieved material | Preservation of critical state, truncation behavior, and refusal to invent missing facts |
| Retries | Fail a transient call repeatedly, then recover | Bounded retries, idempotency, and accurate status reporting |
| Interrupted execution | Stop workers at each major transition | Exactly-once or safely repeatable effects and correct resumption |
What are the most common long-running failure patterns?
The agent repeats a tool call forever
Inspect whether the tool returned a stable error, whether the retry counter is persisted, and whether the planner can see the previous failure. Add a bounded retry policy and a terminal state that requires escalation or a user decision.
The agent invents progress after a timeout
Check whether the state machine distinguishes “request sent” from “result confirmed.” On uncertain completion, query an idempotent status endpoint or mark the operation as unknown; never convert a timeout into a success claim.
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Verify that approval identity, scope, timestamp, and expiration are checkpointed separately from conversational text. Revalidate the approval against the current plan before executing the next side effect.
The agent misreads a valid tool response
Capture the exact payload and schema version, then add a run-level test with the same response. Require structured parsing and an explicit “unknown” branch instead of allowing the model to infer fields that are absent.
A fix improves one case but regresses another
Compare candidate and baseline on the entire versioned dataset with identical graders and thresholds. Review per-scenario deltas, especially high-risk cases, rather than accepting a single aggregate score.
What is a practical implementation sequence?
- Instrument first: emit correlated spans for model calls, retrieval, tools, control decisions, errors, limits, and versions.
- Define contracts: document allowed actions, states, success criteria, safety constraints, and operating limits for core workflows.
- Seed the dataset: convert real incidents and recurring manual checks into independently passable scenarios with explicit graders.
- Cover all three levels: add run-level checks for local defects, trace-level tests for complete tasks, and thread-level tests for persistent state.
- Add replay and fault injection: simulate delays, unavailable tools, malformed responses, duplicate delivery, and restarts without production side effects.
- Make execution durable: persist checkpoints and test both successful handoffs and interruption recovery.
- Automate comparison: pin versions, repeat stochastic trials, report per-scenario results, and block releases on defined safety or quality regressions.
- Close the loop: monitor production traces, route new incidents into the dataset, and review grader quality as the agent and environment change.
Harrison Chase of LangChain summarizes the central operational reality: “You don’t know what your agents will do until you actually run them.” Running them with complete, versioned traces turns that uncertainty into evidence you can test, reproduce, and improve.
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How many repeated trials are enough for a stochastic agent test?
There is no universal number because variance depends on the model, sampling settings, and scenario. Begin with repeated runs that make the pass-rate estimate useful for your risk tolerance, then increase trials for safety-critical or highly variable cases and report the uncertainty rather than a single definitive percentage.
Should I log the model’s hidden chain of thought to debug it?
No. You can diagnose most failures with prompts or message identifiers, tool arguments and results, retrieved evidence, state transitions, timing, and evaluator outcomes. Store only the reasoning detail your policy permits, and design graders around observable behavior rather than private internal deliberation.
How can I prevent a replay from sending duplicate emails or payments?
Use a sandbox or a side-effect-free simulator for replay, and give every mutating operation an idempotency key. The test should verify that a repeated delivery is detected or safely ignored before any external commit occurs.
When should a human take over an agent task?
Define takeover conditions in the behavior contract, such as an expired approval, an unsupported intent, an uncertain external side effect, or a safety-policy conflict. Test that escalation preserves the relevant checkpoint and gives the human enough context to continue without rerunning completed actions.
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