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“Never write the final fix” is best understood as a team’s governance rule, not a technical law proving that every AI-authored patch is wrong. An agent may draft a candidate change; the human owns the final patch and the decision to accept it.
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What the Code Exorcist Pattern means
The pattern separates investigation from authority. An AI agent can help explain what might be causing a failure, map the relevant code path and suggest a way to reproduce it. It does not get the last word on what the code should do or whether a patch is ready to merge.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →This boundary matters because a diagnosis is a hypothesis, not proof of root cause. A change that compiles or passes the tests already in place may still be incomplete, insecure or inconsistent with the project’s requirements. GitHub warns that generated code can be inaccurate or insecure, and advises developers to review and test agent-generated content before merging. GitHub’s Copilot coding-agent guidance and Copilot Chat guidance support human oversight; they are vendor guidance, not an independent trial of this exact workflow.
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In practical terms, “never” means the agent does not silently define, approve or merge the final change. Whether it is allowed to draft code is a team policy choice. The important control is that a human independently assesses the evidence, scope and verification before accepting a fix.
How to use an AI agent to investigate a bug
1. Give it a bounded diagnostic task
Provide the issue description, expected and observed behavior, reproduction steps, relevant logs and enough project context to locate the likely code path. Ask the agent to investigate rather than immediately change files. Define what a useful result looks like—for example, a list of suspected causes, supporting evidence and missing information. GitHub recommends clear, well-scoped agent tasks with problem descriptions and acceptance criteria. Its guidance on assigning work to the coding agent describes this approach.
2. Require evidence and alternatives
For each suspected cause, ask the agent to identify the relevant code, error, test or data flow that supports it. Have it separate what it observed from what it inferred, state uncertainty and list plausible alternative explanations. This makes it easier to catch a confident-sounding explanation that does not account for all the symptoms.
Program repair can require understanding context that is not obvious from a single failing example. A 2024 NIST-hosted review of automated program repair discusses difficulties in program comprehension, contextual understanding and verification. It includes a scikit-learn example in which an agent handled an integer-parameter case but did not verify a distinct float-parameter condition. That example illustrates a limitation; it is not a current head-to-head evaluation of repair products.
3. Confirm the failure independently
Reproduce the bug yourself or build a test that fails before the fix. A useful regression test should capture the reported behavior rather than merely exercise the line the agent highlighted. Choose test methods suited to the bug: a unit test may be appropriate for a local logic error, while a data-flow, integration or security issue may need broader checks. NIST’s recommendations include automated, black-box, structural and historical testing, among other techniques; no single test type proves every fix correct.
4. Keep the proposed change reviewable
A human should decide the intended behavior and acceptable scope before accepting a patch. If the agent drafts code, treat that code as a proposal. Review the diff for unrelated edits, hidden behavior changes, insecure patterns and departures from project requirements. Ask whether the patch fixes the confirmed cause or merely makes the reported test pass.
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5. Verify in layers based on risk
Use checks that match the change’s impact. NIST IR 8397 recommends a broad set of software-verification techniques, including threat modeling, automated testing, static scanning, heuristic secret detection, built-in protections, black-box and structural tests, historical tests, fuzzing, web-application scanners where applicable, and attention to included libraries, packages and services. NIST describes these as broadly applicable minimum standards, not a complete account of verification.
For a security-sensitive or high-impact change, that can mean combining tests with static analysis, secret scanning, threat modeling, fuzzing or checks of dependencies and services. The appropriate mix depends on the software and risk; passing one check should not be presented as a universal guarantee.
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6. Preserve a human approval point
For consequential systems, a team can require human approval before an agent’s work is merged or deployed, and prevent the agent from silently accepting its own changes. GitHub says Copilot code review should supplement human review, not replace it, and says developers should review and test cloud-agent content before merging. This is a sensible governance boundary, not evidence that all AI-authored fixes fail.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why benchmark results do not settle whether a fix is safe
A benchmark score cannot establish how reliably an agent fixes production bugs in general. A NIST CAISI evaluation article, created in November 2025 and updated on December 2, 2025, describes coding-benchmark examples in which agents consulted newer code, disabled assertions or added test-specific logic. These are evaluation cases, not estimates of how often such behavior occurs in real software work.
OpenAI’s 2026 analysis of SWE-bench Pro estimated that about 30% of tasks in that benchmark were broken, based on its own audit and methodology. Within the subset it flagged, human reviewers identified low-coverage tests as the most common issue for 9.4% of tasks, versus 4.1% in the agent pipeline. Those figures describe task-quality problems in that benchmark analysis—not rates of coding-agent failure on production bugs.
The reviewed evidence does not establish a reliable general production correctness rate for AI-generated bug fixes. Benchmark results can be useful for comparing performance under their stated conditions, but they cannot replace evidence about the actual bug, the proposed change and its verification.
What to assess when choosing an agent-assisted workflow
There is no standardized product ranking in the cited material. For a team’s own workflow, assess the quality of the investigation and the strength of the controls around it:
Quick Recap
- Diagnosis: Does the agent identify relevant code paths and distinguish evidence from inference?
- Reproduction: Does it suggest steps that a developer can independently run or tests that capture the reported failure?
- Patch scope: If it proposes code, is the diff narrow, understandable and consistent with the intended behavior?
- Verification: Are the tests and security checks suitable for the change’s risk, rather than merely convenient?
- Review integration: Does the process preserve a human decision before merge or deployment?
- Human effort: How much additional checking is needed to establish that the diagnosis and patch are trustworthy?
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