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An AI coding agent is usually stuck when its loop cannot observe a meaningful change or cannot recognize that the task is complete. Stop the run, preserve the transcript and diff, identify whether the repetition comes from the model, a tool, the test environment, or permissions, then retry with a measurable definition of done and a hard retry limit.
This guide shows how the loop works, how to diagnose each failure pattern, and how to recover in Codex CLI, Visual Studio Code with Copilot, and other agent harnesses without throwing away useful evidence.
Why does an AI coding agent get stuck?
An agent repeatedly cycles through the same stages: the model receives the current conversation and task state, requests one or more tools, the harness executes those tools, and the results are sent back for another model decision. The cycle ends only when the model stops requesting tools and returns a final response. OpenAI describes this as repeated model calls, tool execution, handoffs and stopping points; Visual Studio Code presents the same pattern as request, context, tool action, validation and review.
A stuck run is therefore a control problem, not necessarily a bad code change. If the agent cannot see a changed file, a reduced error count or a new diagnostic, it has no evidence that another attempt would be different. An underspecified task makes this worse: without a success condition, the model may continue editing indefinitely because it cannot prove that it is finished.
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- Repeated tool calls: the same command, with the same arguments, runs again.
- Unchanged artifacts: the diff, test output or error count is identical after several turns.
- Growing cost without progress: tool calls and tokens increase while no subtask is completed.
- Persistent environmental errors: a missing dependency, blocked network request or unwritable file produces the same result every time.
What should I do immediately when the agent loops?
Stop the current run before allowing another identical attempt. Preserve enough evidence to compare the failed iteration with the next one.
- Cancel safely. In Codex CLI, press
CTRL-Cto cancel the current step. In another interface, use its cancel or stop control rather than closing the terminal abruptly. - Save the state. Keep the session transcript, current diff, command output and the last complete error message. Do not reset or discard edits until you know whether they contain a useful partial fix.
- Classify the failure. Record which of these applies: the same command repeats; a test result is unchanged; a tool returns an error; a file is not writable; a dependency is missing; or network or sandbox access fails.
- Inspect the exact evidence. Find the prompt sent to the model, tool name and arguments, tool result, exit status and any changed files. A summary such as “it failed again” is not enough to distinguish a model decision from a harness failure.
- Change one variable before retrying. Supply the missing file or error context, fix the environment, narrow the task or run the failing command manually. Asking for an identical retry with identical inputs usually recreates the loop.
How can I tell whether the model, a tool or the environment is responsible?
Compare consecutive turns in order. The first point at which the state stops changing identifies the layer that needs attention.
| Observed pattern | Likely layer | Next check |
|---|---|---|
| The model proposes different edits, but the file never changes | Tool or permission layer | Check the tool result, path, sandbox write permission and whether the edit command actually ran. |
| The same tool and arguments recur with identical output | Planning or completion logic | Add a stop condition, provide the missing diagnosis and bound retries. |
| A command runs and returns the same nonzero status | Project environment | Run the command yourself and inspect dependencies, working directory, certificates and configuration. |
| The tool reports timeout, authentication or connection errors | Network or service integration | Check connectivity, proxy and certificate settings, credentials, sandbox network policy and MCP server status. |
| Files change, but the validation result never improves | Code or test diagnosis | Compare the diff with the failing assertion and ask for one hypothesis at a time rather than another broad rewrite. |
| Instructions are ignored or tools are missing | Context or configuration discovery | Inspect loaded project rules, skills, session events and tool registration. |
This classification prevents a common mistake: rewriting code to compensate for a command that never executed, or changing prompts when the real problem is a read-only workspace.
How do I inspect an agent run instead of guessing?
Use event-level logs whenever possible. A useful record contains one entry for each model turn and the tool requests and results attached to it.
Capture the model decision
Check the exact instruction and context visible to the model. Confirm that the relevant file, failing output and project rules were actually included. If a rule exists only in a document the harness did not load, repeating the instruction in your prompt may be necessary while you correct configuration.
Capture the tool payload and result
For every repeated call, compare the tool name, arguments, working directory, exit code, stdout, stderr and returned file content. Two calls that look similar in a chat transcript may differ in a path, branch, environment variable or hidden permission error.
Capture termination
GitHub’s Copilot SDK exposes an event pair for each model turn and emits session.idle when processing ends. Equivalent session-event or tool-call logs in another harness should let you see turn count, the final tool result and whether the model explicitly stopped. If no termination event appears, investigate the runner or transport rather than the code change.
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What environment checks prevent a false coding loop?
Before changing the prompt, verify that the agent can perform the operation it is attempting.
- Workspace access: confirm the target directory is inside the allowed sandbox and that the process has read and write permission.
- Working directory: run the failing command from the same directory and with the same environment variables the agent uses.
- Dependencies: install or restore the project’s declared packages, language runtimes and native tools; then rerun the command manually.
- Network: test the required endpoint, proxy and certificate chain. A blocked registry or API can look like a code failure.
- Project instructions and skills: verify that repository rules, agent skills and tool definitions were discovered and loaded for the current session.
- MCP integrations: check that each required server is running, authenticated and returning responses rather than timing out.
- Branch and generated files: ensure the agent is editing the intended checkout, not a different worktree or a generated file that is overwritten on every build.
Google’s agent troubleshooting guidance recommends checking MCP and skills status and restarting the editor or terminal after correcting configuration. Restart only after saving the transcript and diff; otherwise you lose the evidence needed to identify the original fault.
How do I fix a loop in Codex CLI?
For a Codex CLI run that is stuck or timing out, cancel the current step with CTRL-C, retain the session state, and then continue with a narrower request after checking connectivity and sandbox access.
- Press
CTRL-Conce and wait for the prompt to return. Avoid killing the terminal process if the interface can cancel the active step cleanly. - Inspect the last command, its exit status and the files changed since the run began.
- Test the failing command manually in the same project directory. If it cannot reach a package registry, API or local service, fix that condition first.
- Check whether the sandbox permits access to the required directories and network destinations. A denied operation should not be answered with another code edit.
- Resume with an explicit next action, such as “run this command once, report the complete output, and stop if the exit code is unchanged.”
If the same tool call recurs after those checks, stop again and reduce the scope to one file or one failing test. Include the exact error and state what evidence would count as progress.
How do I diagnose repetitive behavior in Visual Studio Code and Copilot?
Visual Studio Code provides separate diagnostics for slow responses, ignored instructions, missing tools and MCP failures. Use the diagnostic that matches the symptom rather than treating every delay as a model problem.
Slow or repetitive response
Open Agent Debug Logs and compare turn-by-turn requests, tool payloads and results. Look for identical calls, a tool that never returns, or a large context being resent without new information.
Instructions appear to be ignored
Open Chat Debug and verify which system, workspace and conversation instructions were applied. If a repository rule is absent, correct its location or configuration before asking the agent to obey it again.
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A required tool is missing
Inspect session events and the relevant output channels to confirm whether the tool was registered, rejected or unavailable in the current session. Do not substitute a guessed command for a tool that the harness never exposed.
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Review MCP server output and network diagnostics, correct authentication or transport settings, then restart the editor or terminal so the updated configuration is loaded. Preserve the old logs in case the failure returns.
What should I check in another agent harness?
The product names differ, but the evidence order is stable:
- Read the session history and identify the first unchanged turn.
- Compare exact tool calls and returned data.
- Verify sandbox, filesystem, dependency and network permissions.
- Confirm that project instructions, skills and integrations were loaded.
- Retry once with a bounded objective and human approval still enabled.
Google’s guidance for several popular agents follows this same pattern and recommends restarting after MCP or skills configuration changes. If the harness has no event log, enable the most detailed session logging available before another attempt; without observability, you cannot tell whether a new run made progress.
How should I write a prompt that gives the agent a stopping point?
State a measurable definition of done before the first tool call. A useful task specification names the scope, validation command, expected result and failure behavior.
Goal: fix the failing parser test in src/parser.ts only.
Done when: npm test -- parser passes and the diff contains no unrelated files.
After each iteration report: files changed, command run, exit code, error-count change and next hypothesis.
Stop when: the test passes, the same tool call occurs twice without new evidence, or a permission/network error appears.Replace the example path and command with values from your project. The important parts are the observable artifact, the validation step and the explicit stop rule, not the wording.
How do I bound retries and detect no progress?
Use both a maximum iteration count and a completion predicate. Microsoft Agent Framework guidance is explicit: “Always bound autonomous loops.” A bound limits damage when the model or tool behaves unexpectedly; a predicate tells the runner what success looks like.
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- Maximum iterations: choose a small limit appropriate to the task and require approval before extending it.
- Completion predicate: define the exact test, build, lint result or artifact that proves completion.
- No-progress rule: stop when the same tool and inputs recur twice without a changed artifact or new diagnostic information.
- Iteration report: require changed files, tests run, error-count delta and the next hypothesis after every cycle.
- Human gate: pause before destructive commands, broad refactors, dependency upgrades or edits outside the stated scope.
Do not treat a higher retry limit as a fix. It only gives an unproductive loop more opportunities to repeat.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I review changes without creating another loop?
Review the diff at each meaningful checkpoint and tie every edit to the current failure. Reject changes that do not alter the failing path, test setup or documented environment condition.
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Use a narrow hypothesis
Ask what single observation the next edit is intended to explain. For example, a type error may justify changing a type boundary; it does not justify rewriting unrelated modules.
Validate the smallest useful unit
Run the failing test or command first, then expand to the broader suite after the local error changes. This produces a clear error-count delta and reduces the chance that unrelated failures hide progress.
Preserve rollback points
Keep commits or patches between meaningful attempts. If a new edit increases failures, restore the last known state rather than asking the agent to undo a long chain of speculative changes.
When should I stop autonomous repair and ask a human?
Escalate when the agent lacks information or authority that a prompt cannot supply. Human review is appropriate when the same call repeats twice without new evidence, the required change crosses an unclear architectural boundary, credentials or production data are involved, or tests and requirements conflict.
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Provide the reviewer with the transcript excerpt, current diff, exact command and output, environment checks already performed, and the decision you need. This turns “the agent is stuck” into a bounded engineering question that can be answered without repeating the failed loop.
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What is the practical recovery checklist?
- Cancel the run and preserve transcript, diff and last error.
- Classify the symptom as repetition, unchanged validation, tool failure, permissions, dependencies or network.
- Compare exact model turns, tool arguments and results.
- Verify workspace, working directory, dependencies, certificates, network and sandbox policy.
- Check project instructions, skills and MCP registration.
- Run the failing command manually once.
- Retry with one changed variable, a measurable completion predicate and a maximum iteration count.
- Stop after two identical no-progress calls and request human review.
The goal is not to make an agent try harder. It is to make every iteration observable, constrained and capable of proving either progress or failure.
Frequently Asked Questions
Can an AI coding agent be stuck even when it keeps changing files?
Yes. It may be making speculative edits that do not affect the failing assertion, or a build step may overwrite those edits. Compare the final artifact and validation output, not just the number of files touched.
Should I delete the agent’s changes before restarting?
Not immediately. Save a patch or commit first, then inspect whether any part of the diff addresses the error. Removing everything can destroy the only useful clue about what the agent already tried.
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The model may have received stale, truncated or unrelated output, or it may have run the command in a different directory. Match the logged working directory, exit code and complete output to the claim before accepting it.
Is increasing the context window a reliable cure for repetition?
No. More context can preserve useful diagnostics, but it cannot supply a missing permission, dependency or completion rule. Fix observability and the environment first, then provide only the files and errors needed for the bounded task.
What information should I include when reporting a stuck run?
Include the harness and version, operating system, task prompt, turn count, repeated tool call, working directory, exit code, complete error, current diff and any network or sandbox restrictions. Redact secrets while retaining the exact error structure.
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