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If an AI agent appears stuck, repeats a tool call, or chooses the wrong tool, inspect its execution history before changing the prompt or retrying. The agent may be waiting for your approval, still running a command, taking a normal amount of time, or genuinely stalled. Once you know which state it is in, use the trace to find the first failure, then change one relevant condition and test again.
This approach treats the agent as an observable loop: model decision, tool invocation, application execution, result returned to the model, and either another step or a stop. A failure at any handoff can look like a model problem. Separating those stages keeps you from “fixing” a prompt when the actual issue is a missing result, an ambiguous schema, or an unbounded retry.
Why does an AI agent seem stuck?
“Stuck” describes several different situations, and they call for different fixes. Check the agent’s current state and execution history first: an unanswered question or approval request is not a model failure, and a long-running command is not necessarily a hang.
Check whether the agent is waiting
Look for a pending question, an approval request, or a terminal command that is still running. VS Code’s recovery guidance calls out these states when an agent appears idle. If approval or an answer is pending, respond to it rather than starting a duplicate run. Also check whether a guardrail paused the action; a safety or policy intervention can be an intentional stop, not a runtime fault.
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Write down the visible state before interacting with the run. Note whether the interface says waiting, executing, awaiting approval, or finished with an error. If multiple agents or subagents are involved, identify which one owns the pending step. A parent agent can appear idle while a child is still executing or waiting for a handoff.
Distinguish slow execution from a stall
Inspect whether the execution history shows progress and compare the elapsed time with the work being performed. Microsoft Discovery documentation, accessed in 2026, gives platform-specific examples: a first tool call can take 2–4 minutes during cold start, subsequent calls are often under 1 minute, and heavy computation can take 5–15 minutes. These are examples for that platform, not general guarantees or service-level targets.
Look for advancing timestamps, a command that is producing output, token or event counters that continue to change, and a tool status that eventually transitions. A long interval with no new event, no result, and no state change is more suspicious than a long interval accompanied by active computation. Do not use the documented durations as universal timeout values; calibrate thresholds to your own tool and runtime.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchIf the run has exceeded what its context suggests is reasonable and the history shows no progress, investigate the model or runtime layer. Capture the trace before retrying: restarting may remove the clearest evidence of what happened. Preserve the original run identifier and timestamps so a later attempt can be compared against it.
What should I check before retrying?
Record the run’s state and evidence before changing anything. A complete trace helps distinguish a bad plan, a bad tool definition, an invalid call, a tool-result problem, and a runtime failure. It also prevents a “successful” retry from hiding the conditions that caused the first failure.
Capture the evidence in order
- The user’s goal and the system and developer instructions in effect.
- The model’s output, including the selected tool and its exact arguments.
- Schema or validation errors, plus the raw tool output and its shape.
- Timestamps, elapsed time, approvals or pending questions, retries, and the final state.
- The active model, tool-choice setting, stop settings, and any context or attachments supplied to the run.
- Agent and subagent identities, handoff events, guardrail decisions, and cancellation or timeout events.
Keep both the request as the model emitted it and the request after application parsing or normalization. A parser can alter a value, drop a field, or associate a result with the wrong call. Those details matter when a call looks correct in the model transcript but fails in the application.
In VS Code, the debug views provide different parts of this picture: Logs show event order; Summary shows duration, token, and error information; Flow Chart shows agent and subagent interactions; and Chat Debug exposes the prompt, context, tools, and response. Use the views together when an event sequence alone does not explain why the agent chose an action. Export or copy the relevant views before clearing a session.
Find the earliest failure that made recovery impossible
Microsoft Research’s AgentRx frames diagnosis around the first critical failure in a trajectory rather than the last visible symptom. Its 2026 announcement reports improvements over prompting baselines of 23.6% in failure localization and 22.9% in root-cause attribution. The AgentRx Benchmark included 115 manually annotated failed trajectories. Those figures describe the reported evaluation, not a guarantee that every agent trace will be diagnosed correctly.
AgentRx’s nine failure categories offer a useful vocabulary for reviewing a trace:
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- Plan Adherence Failure: the agent does not follow its plan.
- Invention of New Information: it introduces information not supported by the available evidence.
- Invalid Invocation: it makes an invalid tool call.
- Misinterpretation of Tool Output: it reads a result incorrectly.
- Intent–Plan Misalignment: its plan does not serve the user’s goal.
- Under-specified User Intent: the request does not establish enough detail to act reliably.
- Intent Not Supported: the system cannot carry out the requested intent.
- Guardrails Triggered: a safety or policy constraint stops the action.
- System Failure: a system-level problem prevents progress.
Use the earliest category that explains why the run could no longer recover. Later errors may be consequences of that first mistake, not separate root causes. For example, an invalid invocation can produce an error result, which the model then misinterprets, which in turn causes repeated calls. Labeling all three as independent root causes obscures the first repairable step.
Why does an agent choose the wrong tool?
Start with the tool definitions, not a longer prompt. Anthropic’s troubleshooting guidance identifies ambiguous descriptions, colliding names, and overly generic schemas as causes of wrong-tool calls. Make the choice between tools legible in the definitions the model receives.
Make tool choices distinct
- Give each tool a unique name that does not collide with another tool’s name.
- State when to use each tool, including the situations in which it is the appropriate choice and, when useful, when not to use it.
- Use concrete input examples so the expected arguments are clear.
- Make schemas specific and strict where the framework supports that behavior.
For example, if one tool reads data and another changes it, say explicitly which one is read-only and which one is for approved mutations. If two tools operate on different environments, identify the environment in each description rather than giving both a generic “manage resource” label. If an input must be one of a small set of values, encode that constraint in the schema instead of relying on prose.
Keep names and descriptions stable while testing. Renaming several tools, changing their schemas, and rewriting the system prompt at the same time makes it impossible to tell which change affected selection. A compact, concrete example in a tool definition can be more diagnostic than a broad instruction repeated in the prompt.
Check whether the tool call was valid
Compare the selected tool and exact arguments with its name, description, and schema. If the call is invalid, note the specific validation error and determine whether the definition permitted an ambiguous or malformed request. If the call is valid but the agent still makes the wrong decision, inspect whether the tool descriptions clearly distinguish the situations in which each one should be used.
Separate selection errors from argument errors. Choosing tool B when tool A was required is a routing problem. Choosing the correct tool with an invalid value is a schema or planning problem. A correct call that produces an unexpected result is an execution or interpretation problem. Recording these separately makes the symptom-to-fix mapping more precise.
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A tool call is a handoff between the model and the application, not an action the model executes by itself. Anthropic puts it plainly: “The model never runs anything on its own.” The application must execute the structured request and return the result so the interaction can continue.
Verify the result contract
For client tools, return one correctly formatted tool_result for each tool_use, in the expected order, then continue the loop. If a result is missing, malformed, or associated with the wrong call, the agent may not progress correctly even when its original request was valid.
Inspect the raw tool output and the result passed back into the loop. Confirm that each result corresponds to its call and that its shape is what the receiving application and agent expect. A useful trace records both the tool’s output and the returned result, so you can tell whether a failure occurred during execution or while handing the result back.
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When several calls are outstanding, preserve their identifiers and ordering. Do not silently convert an exception into an empty success response; the model needs a truthful result or an explicit error to reason about the next step. Likewise, do not return a result for a call that was never executed. These contract violations can produce apparent hangs, invented state, or repeated attempts.
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Put explicit bounds on the loop. If the agent repeats an unsuccessful action, stop automatic retries, preserve the first error, and check whether the next attempt changes the tool, arguments, or relevant conditions.
Set a retry budget and detect duplicates
Choose a bounded retry count appropriate to the workflow, and detect repeated calls with identical tool-and-argument pairs. A repeated signature is a practical signal to pause and inspect the trace instead of letting the same unsuccessful action run again. Do not treat every repeated tool name as a duplicate: the important comparison is the tool together with its arguments and the run’s context.
Include the result and error state in your duplicate analysis. A call may legitimately repeat after a transient failure if the application changed a condition or the tool returned new information. Conversely, a call with a slightly different argument can still repeat the same failed assumption. The retry budget should therefore be paired with a rule requiring analysis or a changed condition, not used as the only safeguard.
Use the framework’s stop behavior deliberately
OpenAI Agents SDK documentation says automatic reset of toolChoice to auto after a tool call prevents an infinite loop. Where the workflow permits it, define a stop condition such as stop_on_first_tool or stopAtToolNames. These controls govern when the loop yields or stops; they do not replace diagnosing why the model is selecting a tool.
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Check whether a forced tool choice is still active after a call. A setting intended to require one operation can accidentally require it on every turn. Reset or narrow that setting when the workflow should allow a final response, a different tool, or a clarification question.
Protect side effects
Pair loop controls with application-level idempotency and side-effect checks. A repeated request can be more than wasted time if executing it again changes something. Before allowing retries for an action with side effects, decide how the application will prevent or detect unintended duplicate effects; a retry cap alone does not make repeated execution safe.
For operations that create, send, delete, or otherwise mutate state, require the application to recognize an already-completed request where possible. Log the effect check and its outcome. If the operation cannot be made idempotent, make the stop or approval boundary explicit before another attempt.
How do I match symptoms to a fix?
Use the trace to target the layer that failed. The symptom is a starting point, not proof of a particular cause.
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| Symptom | Likely cause | Targeted check or fix | Evidence |
|---|---|---|---|
| Calls tool A instead of tool B | Ambiguous descriptions, name collision, or generic schema | Clarify when each tool should be used, make names unique, add concrete examples, and tighten the schema where supported. | Anthropic troubleshooting guidance |
| Repeats identical calls | No stop condition, forced tool choice, unchanged result, or no retry budget | Reset tool choice where appropriate, cap retries, detect duplicate tool-and-argument pairs, and stop at defined tool names when the workflow allows it. | OpenAI Agents SDK and runtime-loop documentation |
| Appears idle | Pending approval or question, active command, or stalled model response | Check current state, execution history, and logs before restarting. | VS Code recovery guidance and Microsoft Discovery documentation |
| Tool call never progresses | Missing or malformed tool result | Return one correctly formatted result for every call, in the expected order, and continue the loop. | Anthropic tool-use loop documentation |
| Acts on a wrong assumption after a tool returns | Misinterpreted output or an ungrounded state update | Log the raw output, validate its shape, and identify the first interpretation failure. | Microsoft Research AgentRx |
| A fix appears ineffective | Several variables changed at once or no comparable trace is available | Change one condition, rerun a comparable case, and inspect the same debug views. | VS Code debug guidance |
How should I test a fix?
Change one relevant condition and rerun a comparable case. If you change the tool description, schema, stop rule, and context together, an improved run will not tell you which change mattered.
- Preserve the original trace. Record the first error, the tool and arguments involved, and the state immediately before the failure.
- State the disproven assumption. Identify what the failed call or result showed was not working, rather than retrying the same action without a new basis.
- Pick one change at the failing layer. For example, clarify a tool description, constrain a schema, add a stop rule, or attach relevant context.
- Rerun a comparable case. Keep the goal and relevant conditions sufficiently similar to make the resulting trace useful for comparison.
- Inspect the same evidence. Check whether the agent selected a better tool, returned valid arguments, handled the result correctly, and reached an appropriate final state.
VS Code’s recovery guidance recommends requiring analysis before another attempt when the agent repeats the same unsuccessful action. That pause matters: a retry is useful when a relevant condition has changed, not merely because the previous attempt failed.
For a fair comparison, keep the model and tool versions, user goal, available tools, and relevant context constant unless one of those is the variable under test. Compare elapsed time, tool-call count, validation errors, result handling, and final state. A run that finishes faster but reaches the wrong state is not a successful fix.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which debugging approach should I use?
Choose the approach that exposes the layer you need to inspect. A useful debugging setup makes prompts, tool definitions, calls and results, validation, stop behavior, approvals, latency, and handoffs visible enough to reconstruct the run.
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- For an idle-looking run: prioritize current state, execution history, approvals, commands, and timestamps.
- For a wrong tool: prioritize tool names, descriptions, examples, schemas, and the model’s selected arguments.
- For a stalled tool handoff: prioritize the call-to-result mapping, formatting, order, and raw output.
- For repetition: prioritize retry counts, duplicate call signatures, tool-choice behavior, stop conditions, and side effects.
- For multi-agent workflows: include handoffs in the trace so an error in a subagent or transition is not mistaken for a model stall.
When evaluating an implementation or debugging setup, compare its visibility of prompts and tool definitions; completeness of tool-call and result traces; schema strictness and validation; configurable stop and retry behavior; idempotency and side-effect controls; approval and guardrail visibility; latency and error metrics; and support for multi-agent handoffs. These are practical comparison criteria, not a claim that one framework is best for every workflow.
An observability or trace-debugging platform can be useful when native logs do not expose those fields. Look for searchable tool-call logs, prompt and context inspection, timeline views, result-contract errors, retry counters, and exportable traces. Verify a vendor’s current program, geography, pricing, and terms before making a purchasing or affiliate recommendation; the right choice depends on the visibility and controls your workflow requires.
What should a production checklist include?
Before treating an agent as reliable, make the controls explicit in the application rather than relying on a model instruction alone.
- State inspection that distinguishes waiting, executing, completed, and stalled runs.
- Trace capture for prompts, context, tool definitions, arguments, results, errors, approvals, retries, and final state.
- Unique tool names, “when to use” descriptions, concrete examples, and strict schemas where supported.
- Validation of every invocation and a correctly formatted, correctly ordered result for every client tool call.
- A retry budget, duplicate tool-and-argument detection, deliberate tool-choice reset, and defined stop conditions.
- Idempotency or side-effect checks for operations that change external state.
- Comparable reruns that change one condition and preserve the original evidence.
These requirements turn a vague complaint—“the agent froze”—into a testable question: which state was active, which event failed first, and which control should have prevented the next incorrect step?
Frequently Asked Questions
Should I retry an AI agent run before saving its logs?
No. Save the trace first, especially when a failure is intermittent or the next run may overwrite execution history. The original tool arguments, output, and timestamps can help distinguish a changed condition from the same failure repeating.
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How can I tell whether the model or my application caused a failed tool call?
Compare the model’s structured request with the application’s execution and returned result. If the request is invalid against the tool definition, investigate selection or schema design; if it is valid but the result is missing or mismatched, inspect the application’s handoff.
What is the most useful detail to capture when an agent repeats itself?
Capture the tool name and exact arguments for each attempt alongside the result and timestamp. Comparing those fields shows whether the agent is repeating the same request or making a changed attempt that only looks similar.
How long should I wait before deciding an agent is hung?
There is no universal timeout. Check for progress and compare the work with your runtime’s normal behavior. Microsoft Discovery documents examples of 2–4 minutes for a first cold-start tool call, under 1 minute for later calls, and 5–15 minutes for heavy computation; these are platform-specific guidance, not general guarantees.
Why does resetting tool choice help with repetition?
A forced tool choice can keep selecting the same operation after its result returns. OpenAI Agents SDK documentation describes resetting toolChoice to auto after a tool call as a way to prevent an infinite loop, while still requiring retry limits and diagnosis of the underlying selection.
What if the tool call is valid but the agent still chooses the wrong action afterward?
Inspect the raw result and the result passed back into the loop. Validate the result shape and identify whether the model misinterpreted the output or updated state without evidence. Classify the earliest interpretation failure rather than only the final repeated call.
Do stricter schemas always solve wrong-tool selection?
No. Strict schemas can prevent malformed arguments, but they do not by themselves explain when a tool should be used. Use unique names, clear “when to use” descriptions, concrete examples, and schema constraints together, then compare a trace from a comparable rerun.
How do I debug a multi-agent handoff that looks like a stall?
Include each agent and subagent in the flow trace, with handoff timestamps, selected tools, results, approvals, and final states. A parent can appear idle while a child is running, waiting, or failing, so inspect the handoff event before changing the parent prompt.
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Treat a stuck, repetitive, or wrong-tool run as an observable loop. Check whether it is waiting or merely slow, save a complete trace, verify the tool definitions and result contract, bound retries, protect side effects, and locate the first unrecoverable event. Then change one condition and compare a similar rerun.
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