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If an AI coding agent keeps making the same edit, undoing your correction, or producing mutually conflicting changes, stop the current loop before asking it to try again. Inspect the workspace and complete diff, identify the failed assumption, apply one narrowly worded correction, and verify the result with tests before integrating it. Repeating an unchanged request usually repeats the same mistake.
This sequence works across agent products, but controls such as steering, checkpoints, request editing, and context compaction are product-specific. The names and availability of those controls can change, so treat the product examples below as patterns and confirm the current documentation for your agent.
What repeated or conflicting edits usually mean
A loop is not simply an agent being “stubborn.” It is evidence that the agent’s action and your intended outcome have diverged, or that the state it is reading is not the state you think it is. Common causes include:
- A failed assumption: The agent assumes a file, API, package version, command output, or directory layout that is different from reality.
- An unchanged retry: The same command or edit is attempted after an error without changing the hypothesis that caused the error.
- Conflicting context: Earlier instructions, later corrections, workspace files, referenced documents, and tool output point in different directions.
- Multiple edit paths: A change may be made through an editor tool, a terminal command, a script, or an MCP tool. An editor’s pending-change indicator may therefore omit part of the actual workspace change.
- State drift: Another process, branch operation, formatter, generated-file step, or human edit changes the files between agent turns.
First determine which state you are in: the agent may still be running, waiting for your answer or approval, waiting for a terminal command, or finished. The safest intervention depends on that distinction.
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For product-specific examples, Visual Studio Code’s guidance on getting an agent back on track describes steering an active request, following up after completion, and editing an earlier request where supported: Get an agent back on track.
The six-step recovery sequence
1. Stop the loop and inspect the real state
Do not send another “try again” message while an unsafe or redundant operation is still running. Pause or cancel it using the control provided by your agent, then establish what is actually happening.
- Check whether the agent is processing, waiting for a question, waiting for approval, or blocked on a terminal command.
- Open the agent’s changed-file list, if it provides one.
- Inspect the complete diff, including files changed outside the editor view.
- Check the repository state from a terminal when appropriate:
git status --short
git diff --stat
git diff --check
git diffgit diff --check catches whitespace errors; it does not prove that the code is correct. If your project uses generated files or untracked files, inspect those separately because a normal tracked-file diff may not show them. Agent edits can arrive through editing tools, shell commands, scripts, or MCP tools, so a clean-looking editor decoration is not a reliable inventory of every change. Visual Studio Code explains its review and revert workflow here: Review and revert agent changes.
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Capture the first unexpected change and the first relevant error before doing more work. Those two observations often identify whether the problem is an incorrect instruction, an environmental failure, or an interaction between edits.
2. Name the failure before retrying
Write down the requested outcome in one sentence, then compare it with the diff and the observed behavior. Describe the mismatch in terms the agent can act on:
- Wrong target: “The change was made in
legacy-router.ts, but the request applies only tosrc/router.ts.” - Wrong behavior: “The endpoint now returns a 200 response for invalid input; it must return 422 and preserve the existing error schema.”
- Wrong constraint: “The patch adds a dependency, but this task must use the packages already in
package-lock.json.” - Environmental failure: “The test command failed because the service on port 5432 was unavailable; no source change addresses that failure.”
- Conflicting instruction: “The earlier request said to preserve the public method signature; the later edit removed it. The preservation requirement is authoritative.”
If the same action failed twice, treat that as a diagnostic signal. Change the hypothesis or gather missing evidence instead of repeating the identical action. Ask the agent to explain the assumption it used only after you have inspected the concrete diff and error; a verbal explanation is not a substitute for workspace evidence.
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3. Apply the smallest correction that matches the state
A correction should identify the mismatch, the allowed scope, and the acceptance check. Avoid broad instructions such as “fix everything” or “make it work.” Use one of these paths:
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Send a focused steering message that can be acted on immediately:
Stop changing files outside src/auth/. Keep the existing public API and revert the new dependency. In validateToken(), handle the expired-token case by returning the existing AuthError. Before editing, show the files you will touch.Use this only when the running direction is safe to interrupt. If it is modifying production data, deleting files, or running an untrusted command, stop it first and inspect the state.
The agent finished and most of the work is useful
Send a follow-up that names the one identifiable mismatch and the expected result:
The parser refactor is useful, but it changed quoted commas inside CSV fields. Preserve the refactor, restore quoted-field handling, and add or update the focused test for "a,b". Do not alter the command-line interface.Point to files, symbols, line-level behavior, or an error message. A follow-up should not silently introduce a new goal; if the goal changed, start a new request or edit the original request where the product supports that operation.
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Clarify the request’s scope, priority, and definition of done. Some interfaces let you edit an earlier request. Editing can roll back that request and subsequent edits, so read the resulting diff and Git state before continuing. The exact rollback behavior depends on the session type and product.
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4. Restore the workspace when later edits are unwanted
If the agent has layered bad changes on top of a known-good point, continuing forward may make the diff harder to reason about. Restore the smallest safe checkpoint, then inspect what remains.
- Use a product checkpoint when the agent session provides one and you need to return files to an earlier session state.
- Use Git for durable history: create a branch or commit a known-good baseline before substantial agent work, and use ordinary source-control operations to recover files when needed.
- Preserve unrelated human work: do not reset or discard the entire workspace until you have separated the agent’s edits from changes you intend to keep.
For example, if all uncommitted changes belong to the failed attempt and you have verified that, you might use:
git diff > failed-attempt.patch
git restore --source=HEAD -- path/to/file1 path/to/file2
git status --shortThat command sequence is destructive for the named files after the patch is saved; adapt it to your branch and working-tree situation. If you have staged changes, untracked files, or work from another contributor, stop and separate those states before restoring anything. A checkpoint or clean rollback point only makes recovery easier; it does not establish that the next result is correct.
When to prefer each recovery method
| Situation | Least disruptive option | What to verify afterward |
|---|---|---|
| Active request is safe but narrowly wrong | Focused steering correction | The agent adopted the boundary before making more edits |
| Completed work has one identifiable mismatch | Follow-up request | The new diff changes only the mismatch and required tests |
| Earlier wording caused the wrong interpretation | Edit the earlier request, if supported | Whether later edits were rolled back and whether useful work remains |
| Several later edits are unwanted | Checkpoint restore or selective Git restore | Working-tree status, preserved human work, and the restored diff |
| Goal or authoritative requirements changed | Compact the relevant state or start a clean session | The new session contains the current goal, constraints, and baseline |
The trade-offs between request editing, checkpoints, and source control vary by session type. The product documentation for your agent is authoritative for the exact controls.
5. Remove context confusion
An agent’s context is more than the latest message. It can include prior conversation, workspace files, referenced documents, tool outputs, and decisions made earlier in the session. If those elements conflict, make the authority explicit.
Before retrying, provide a compact state summary with four parts:
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- Current goal: the one behavior that must change.
- Boundaries: files, APIs, dependencies, performance or compatibility requirements, and things that must not change.
- Decisions to preserve: choices from earlier work that remain valid.
- Evidence: the exact error, failing test, relevant symbols, and the current diff or file list.
Attach or name the relevant files and symbols rather than asking the agent to rediscover the whole repository. If the conversation contains obsolete experiments or contradictory instructions, compact it while retaining the summary above. If the authoritative requirements changed, or you can no longer tell which instruction wins, start a clean session from a known-good workspace and include only the current requirements and necessary references.
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Visual Studio Code’s explanation of agent context covers how conversation, files, tools, and other inputs shape an agent’s next action: Understand context in AI agents. The practical implication is that a correction should repair the relevant context, not merely repeat the desired outcome.
6. Verify the complete result before integration
Once the agent stops changing files, review the complete diff, not just the last response. Look for accidental formatting changes, generated-file churn, API changes, deleted validation, dependency updates, secrets, and edits outside the stated scope.
- Run the project’s formatter or linter in its documented mode.
- Run the smallest focused test that exercises the changed behavior.
- Run the broader unit, integration, build, or type-check commands required by the project.
- Use a debugger or manual reproduction for failures that tests do not cover.
- Review
git status --shortagain, including untracked files. - Commit or merge only after the diff and checks match the intended outcome.
Choose commands from the project’s own scripts rather than inventing a generic test command. For a JavaScript project, that may mean inspecting the scripts in package.json; for another language, use its established build and test entry points. Record any check you could not run and why, instead of treating an unexecuted check as a pass.
OpenAI describes the importance of reviewing and controlling the agent loop in Unrolling the Codex agent loop. The general lesson applies beyond one product: let the agent iterate only while each iteration produces evidence that it is moving toward the acceptance criteria.
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| Symptom | Likely state | Action |
|---|---|---|
| Agent appears idle | Pending question, approval, or terminal command | Inspect the run status and answer or approve only after checking the proposed operation. |
| Agent is still working but following a safe, incorrect approach | Active request with a narrow direction error | Send a focused steering correction; stop the run first if the operation could cause harm. |
| Useful work is complete with one mismatch | Finished request | Follow up with the exact mismatch, expected behavior, and a targeted check. |
| An ambiguous request produced the wrong result | Interpretation error | Edit the earlier request if supported, then inspect any rollback of subsequent edits. |
| Later changes are unwanted | Workspace has layered edits | Restore a checkpoint or selectively restore files, preserving unrelated work. |
| The same failed action repeats | Unchanged assumption or environment | Stop, capture the error and diff, state a new hypothesis, and choose a different action. |
| Earlier decisions conflict with current instructions | Context authority is unclear | Declare the authoritative requirement, summarize preserved state, and compact or restart the session. |
How to write a correction the agent can follow
A useful correction is short enough to remain unambiguous but specific enough to constrain behavior. Include these elements in order:
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- Stop condition: “Do not edit files outside …” or “Do not run the migration.”
- Observed fact: quote the failing test, command output, or unexpected diff.
- Required behavior: describe the result, including status codes, error types, compatibility rules, or user-visible behavior where relevant.
- Preserved decisions: state APIs, dependencies, or refactors that must remain.
- Verification: name the focused test or check that proves the correction.
For example:
Do not modify the database schema or add packages. The failing test is tests/cache.test.ts:42: it expects a cache miss after expiration, but the current code returns the stale value. Keep the existing Cache interface, change only the expiration check, and run that test plus the cache unit suite.Do not ask for a broad cleanup in the same turn as a recovery. Once the narrow fix is verified, open a separate request for refactoring or optimization.
Preventing the next conflict
Start from an explicit baseline
Before delegating a substantial change, check the branch, record the working-tree status, and create a commit or other durable recovery point. Tell the agent whether existing uncommitted edits are intentional. This prevents it from treating human work as disposable baseline code.
Define boundaries up front
Name the allowed directories, files, public interfaces, dependency policy, generated-file policy, and required checks. If a task has an explicitly forbidden action, state it as a prohibition rather than assuming the agent will infer it.
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Ask for one coherent change at a time: implementation, focused test, then broader integration. Require a diff review between slices when the change touches security, persistence, public APIs, or build configuration.
Keep external state visible
When a failure depends on a service, environment variable, fixture, tool version, or generated artifact, include that fact in the request. An agent cannot reliably fix an unavailable service by editing application code, and it may otherwise keep changing the wrong layer.
Separate diagnosis from implementation
For a difficult failure, first ask for a concise diagnosis that cites the relevant files and evidence. Then authorize the smallest implementation step. This reduces the chance that a speculative fix becomes a chain of mutually conflicting edits.
Limits of product-specific controls
Steering an active run, editing a prior request, compacting context, and restoring checkpoints are interface features, not universal agent capabilities. One product may expose a visible changed-file review and revert action; another may require Git commands. Checkpoint scope can differ between interactive sessions, background tasks, and terminal-driven workflows. Request editing may also roll back later turns in one interface but not another.
Regardless of the interface, the portable controls are the same: stop unsafe work, inspect the actual diff, identify the failed assumption, make one narrow correction, preserve a durable source-control baseline, and run the project’s checks. No documented comparative reliability ranking or success rate establishes that one coding agent avoids these failures better than another, so choose recovery actions from the evidence in your own workspace rather than from a presumed ranking.
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