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Debug an AI-agent workflow like a distributed system: capture a correlated trace of the entire run, compare it with a successful run, and find the first step that became invalid or unrecoverable. Then classify the fault before choosing a retry, replan, fallback, or human escalation. The final response often only reveals where a failure surfaced—not where it began.
Why does one agent ignore another agent?
Usually, the apparent “ignore” is a broken handoff somewhere in a chain of decisions and operations. The receiving agent may never have received the message, may have received incomplete context, may have interpreted a valid message differently, or may have been blocked from acting by a tool error or policy. A polished final response can hide any of these earlier failures.
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OpenAI’s Agents API documentation describes a trace as the steps within a turn, including model responses, tool calls, and work delegated to other agents. That is a useful minimum: when diagnosing a handoff, inspect the preceding decision and the receiving agent’s actual input, not just the sender’s intended output.
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Distinguish a missing handoff from a bad handoff
- Missing handoff: The trace shows no dispatch, queue event, or receiving-agent invocation. Investigate orchestration conditions, routing, queue delivery, and timeouts.
- Incomplete handoff: A receiving invocation exists, but required task details, constraints, or evidence are absent. Compare the payload with the original user request and the sender’s output.
- Misread handoff: The payload is present and usable, but the recipient’s plan or interpretation does not match it. Check the agent’s reasoning-visible outputs, tool choices, and subsequent actions.
- Blocked handoff: The recipient received the task but could not proceed because of a policy, access control, unsupported intent, or system fault. The trace should record the block or error.
What should I record so I can find the failed run?
Make every execution searchable and retain enough version and timing information to compare it with other runs. Without stable identifiers and configuration metadata, a team may be unable to tell whether two apparently identical failures used the same prompts, model, tools, routing, or deployment.
- Run or session ID, plus user and task identifiers that are privacy-safe for your environment.
- Agent name, model version, prompt version, tool versions, and orchestration configuration.
- Start and end timestamps, deployment region, outcome, retry count, and relevant error codes.
- Routing decisions, handoff identifiers, and links between parent and delegated work.
- Latency, token counts, tool arguments and results, and any fallback or checkpoint events.
- Tags for failure class, workflow, release, or incident so intermittent cases can be found again.
Record the versions that actually served the request, not merely the versions currently deployed. If a prompt or tool changes during an incident, this distinction can separate a regression from a coincidental repeat. Keep sensitive inputs and outputs protected: traces can contain user data, credentials, or business information, so access controls, retention rules, and redaction should be part of the logging design.
How do I trace a multi-agent failure?
Follow one run from its initiating request through every agent, model generation, tool call, external service, queue, memory operation, retry, and final response. A trace is useful only if it preserves the causal links between those events and shows what each step actually received and returned.
- Open the run by its identifier. Start from the session or run ID and confirm the request, timestamp, deployment, and outcome match the incident you are investigating.
- Follow parent and child work. Expand delegated-agent spans and connect them to the root agent. Check whether each expected child invocation exists and whether the parent waited for, timed out, or abandoned it.
- Inspect the transition payloads. For every handoff, compare the sender’s output with the recipient’s input. Include task instructions, user constraints, evidence, required fields, and any context pulled from memory.
- Inspect operations, not just summaries. Review model responses, tool arguments, tool results, queue events, service responses, and memory reads or writes. A summary may omit the exact detail that caused a downstream error.
- Mark timing and retries. Note when each event began and ended, how long a call waited, whether it was repeated, and whether the later attempt used the same arguments or context.
- Continue to the final response. Confirm whether the final answer reflects the executed work or claims an outcome that the trace does not support.
If the trace ends before the failure, treat the missing segment as an observability or infrastructure question rather than assuming the agent made a bad decision. A complete causal record needs both the event sequence and the inputs and outputs at important boundaries.
How do I find the first bad step instead of staring at the final answer?
Walk forward from the user’s goal and stop at the earliest step that violates a requirement, lacks necessary evidence, or makes recovery impossible. Later symptoms may be consequences of that first critical failure, so fixing only the final response can leave the underlying defect intact.
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- Write down the expected outcome and constraints. State what the user asked for, what information or permissions are required, and what the workflow is allowed to do.
- Check the initial plan. Did the agent preserve the user’s intent and required steps? Look for omitted work, added actions, or assumptions unsupported by the request.
- Check each invocation. Is the tool or subagent call valid for its schema? Are required fields present and values correctly typed? Do the arguments match the plan and task?
- Check the returned evidence. Did the agent accurately interpret the tool result, or did it invent, alter, or overlook information?
- Check each handoff. Is the next agent’s payload complete, grounded in available evidence, on topic, and within policy?
- Check for external blockers. Look for access denials, safety controls, unsupported requests, connectivity failures, rate limits, timeouts, and endpoint errors.
- Mark the earliest critical failure. Record the exact event and evidence. Do not label a downstream wrong answer as the root cause if an earlier invalid call or missing handoff explains it.
Microsoft Research’s AgentRx announcement uses the term “Critical Failure Step” for the point to localize. AgentRx combines guarded constraints with an evidence log. Its 2026 announcement reports evaluation on 115 manually annotated failed trajectories across τ-bench, Flash, and Magentic-One, with a 23.6 percentage-point improvement in failure-localization accuracy and a 22.9 percentage-point improvement in root-cause attribution over prompting baselines. Those results describe the reported evaluation; they are not a guarantee that every production failure will be diagnosed correctly.
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What kinds of failure should I record?
Use a consistent classification so incident notes distinguish agent mistakes from missing information, policy decisions, and infrastructure faults. Microsoft Research’s AgentRx announcement defines these nine categories:
| Failure class | What to look for in the trace |
|---|---|
| Plan adherence failure | Required steps were skipped, or actions were added that the plan did not call for. |
| Invention of new information | A claim was altered or hallucinated beyond what the trace and tool evidence support. |
| Invalid invocation | A tool call was malformed or failed its schema requirements. |
| Misinterpretation of tool output | The tool returned usable information, but the agent read it incorrectly. |
| Intent-plan misalignment | The plan did not reflect the user’s goal or constraints. |
| Under-specified user intent | Required information was unavailable, so the workflow could not safely determine what to do. |
| Intent not supported | No available tool or workflow could perform the requested task. |
| Guardrails triggered | Safety controls or access restrictions blocked execution. |
| System failure | Connectivity, endpoint, timeout, or other infrastructure fault interrupted the run. |
Choose the earliest category that explains the evidence, and note downstream effects separately. For example, a malformed tool call may cause an empty result, which a later agent then misinterprets. Recording only the latter obscures the repair: the call schema needs correction before the downstream interpretation can be meaningfully assessed.
How should I validate an agent handoff?
Validate each agent’s output before passing it onward. Microsoft’s Azure Architecture Center recommends validating agent output before passing it to the next agent; in practice, this means checking structure and meaning, not just whether a response exists.
- Schema: Does the output parse and conform to the expected type and format?
- Required information: Are all mandatory fields and task details present?
- Grounding: Can factual claims be traced to the request, tool output, or another permitted source?
- Intent alignment: Does the output still address the user’s goal and preserve stated constraints?
- Confidence and ambiguity: Is there enough information to proceed, or should the system ask the user to clarify?
- Policy and access: Is the proposed next action permitted for this request and identity?
When validation fails, do not silently forward the defective content. Reject it and request a corrected output when the issue is a repairable format or missing field; replan when the task strategy is wrong; ask for clarification when user intent is insufficient; or halt when the request is unsupported or blocked. A validation layer can itself fail, so log its inputs, result, and disposition as trace events.
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When should I retry, replan, fall back, or escalate?
Classify the error before recovery. Retrying makes sense for transient faults; repeating the same invalid action does not. AWS recommends exponential backoff with jitter and a retry budget, while Azure’s architecture guidance emphasizes timeouts, graceful degradation, surfaced errors, circuit breakers, and checkpoints.
| Observed condition | Appropriate response | What to avoid |
|---|---|---|
| Rate limit, overload, or temporary service fault | Retry with bounded attempts, exponential backoff, jitter, and a retry budget. | Unbounded rapid retries that amplify load or hide a persistent outage. |
| Timeout or slow dependency | Apply a suitable timeout; retry only if the operation is safe to repeat and the fault appears transient. | Assuming a timed-out operation did nothing; it may have completed even if the response was lost. |
| Invalid parameters or schema | Correct the arguments or output structure, then validate before sending again. | Retrying unchanged malformed input. |
| Persistent tool or service failure | Use a supported fallback, degrade gracefully, surface the limitation, or escalate to a person. | Claiming success when the intended action was not confirmed. |
| Bad plan, wrong interpretation, or missing context | Replan from grounded evidence, repair the context, or ask the user for clarification. | Repeating the same reasoning path without addressing its cause. |
| Policy block or unsupported intent | Stop, explain the limitation appropriately, or route to an authorized human process. | Retrying in a way that attempts to bypass the block. |
Use checkpoints where a workflow can resume safely after a failure, and define circuit-breaker behavior for dependencies that are persistently unhealthy. For operations with side effects, such as submitting a transaction or changing a record, establish idempotency or verify the operation’s state before retrying: a lost response is not proof that the action did not occur.
How can I tell a latency problem from an orchestration bug?
Measure latency by stage rather than relying on total run time. A slow run may result from model generation, deployment capacity, throttling, a tool or service, queue delay, or serialization; the trace should help separate those causes.
- Time to first token and inter-token time for model responses.
- Generated token count and prompt size.
- Deployment utilization and throttling signals.
- Tool and external-service latency, including timeouts.
- Queue waiting time, handoff delay, and serialization time.
- Whether latency appears in all runs, one agent, one dependency, or one region.
If an agent never starts, inspect routing and queue events. If it starts but takes a long time before responding, compare model timing and prompt size. If the model responds promptly but the workflow stalls, examine tool calls, external dependencies, and orchestration waits. These measurements are diagnostic signals, not proof by themselves; compare them with the event sequence and service error records.
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How do I investigate an intermittent failure?
Preserve the original failed trajectory, then compare it with a successful run of the same task. Reproduction helps, but a fresh run may take a different probabilistic path and therefore fail to reproduce the original cause.
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- Save the failed run’s trace and version metadata as soon as possible, subject to privacy and retention rules.
- Find a successful run with the closest matching task, inputs, and configuration.
- Diff user input, model and prompt versions, tool arguments and results, routing, handoff payloads, timing, and token counts.
- Check whether a changed tool result, missing memory entry, delayed message, or retry explains the divergent path.
- Replay the original trajectory where the system supports replay, or build a fixture that preserves the relevant inputs and event sequence.
- After changing a prompt, model, tool, or orchestration rule, rerun the targeted checks and broader evaluations.
A comparison is only useful when the runs are genuinely comparable. If versions, region, or dependency state differ, record that difference rather than attributing the outcome to a single prompt or model change.
How do I make sure the same failure does not return?
Turn each root cause into a replayable test, not just an incident note. Use per-agent unit tests for local behavior and end-to-end integration tests for routing, handoffs, tools, and recovery paths.
- Test the boundary that failed. For an invalid call, test schema rejection and correction. For a lost handoff, test dispatch and receipt. For a misread result, test interpretation against the recorded evidence.
- Keep a trajectory fixture. Preserve the relevant inputs and sequence so the failure can be replayed without depending on a lucky reproduction.
- Test expected properties. Score whether the workflow satisfied the user’s intent, respected constraints, used evidence, and handled errors—not whether it emitted an identical string.
- Exercise recovery paths. Include transient retries, exhausted retry budgets, timeouts, fallbacks, policy blocks, clarification, and escalation where they apply.
- Re-evaluate after changes. Rerun tests and evaluations after changes to prompts, models, tools, or orchestration.
For probabilistic outputs, exact string equality is often a brittle measure. A rubric or judge can evaluate the relevant outcome and constraints, while deterministic checks should cover what can be verified exactly—such as schema validity, required fields, tool authorization, or whether an action was confirmed. Recurring failures point to a gap in tests or evaluations, not merely an isolated debugging incident.
What should an incident note contain?
A concise incident record should let another engineer verify the diagnosis without reconstructing the investigation from memory. Include the run identifier and relevant version details, the user goal and expected result, the earliest critical failure with trace evidence, the failure category, and the recovery taken.
- Run ID, timestamp, region, agent/model/prompt/tool versions, and retry count.
- Expected behavior and the first event that departed from it.
- Relevant handoff payloads, tool arguments and results, error codes, and timings.
- Root-cause category, downstream symptoms, and whether the conclusion is confirmed or still uncertain.
- Retry, replan, fallback, or escalation decision and its result.
- Test or fixture added to prevent recurrence, plus any privacy or access considerations.
Keep evidence and interpretation distinct: quote or reference what the trace shows, then state the diagnosis it supports. If the trace cannot distinguish between two causes, say so rather than presenting a plausible theory as established fact.
Frequently Asked Questions
Can an AI agent’s trace prove exactly why it made a decision?
Not always. A trace can show the recorded inputs, outputs, tool activity, and timing, but it may not expose every internal factor behind a model response. Treat it as operational evidence for locating failures, and distinguish recorded facts from an inferred explanation.
Should I log full prompts and tool outputs for debugging?
Only when your privacy, security, and retention rules permit it. Use access controls and redaction for sensitive information, and retain identifiers and version metadata even when content must be minimized.
How many times should an agent retry a failed tool call?
There is no universal safe number; set a bounded retry budget based on the dependency, latency budget, and operation’s side effects. A retry policy should stop when attempts are exhausted and hand control to a fallback or clear error path.
Can I make a multi-agent workflow fully deterministic?
The evidence supports better diagnosis and recovery, not a guarantee of deterministic model behavior. Stable fixtures, controlled versions, and explicit validation can make failures easier to reproduce and constrain, but probabilistic components may still take different paths.
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