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Yes—an AI agent can help investigate a bug without being trusted to own its final fix. The “Code Exorcist Pattern” is a team policy: let the agent trace symptoms, inspect likely code paths, suggest causes, and identify what evidence is missing; keep a human engineer responsible for deciding the change, reviewing it, and approving it after verification. The name is a useful rule of thumb, not a proven technical law that AI must never draft code.
That boundary matters because a plausible diagnosis is only a hypothesis, and a patch that passes the current tests can still miss the intended behavior, introduce a security problem, or exploit a gap in the tests. Can AI agents diagnose bugs without being trusted to fix them? Yes, provided the workflow treats their output as evidence and proposals—not authority.
What the Code Exorcist Pattern means
The pattern separates investigation from acceptance. An agent may help explain what appears to be happening and even draft a candidate change, but a human engineer owns the final patch and the decision to merge it.
This is a governance boundary, not a claim that every AI-authored patch is wrong or that an agent is technically incapable of producing a useful fix. GitHub advises that Copilot code review supplement human review, and that developers review and test cloud-agent output before merging. Its guidance supports oversight; it is vendor guidance, not an independent trial of this named workflow. GitHub’s agent guidance
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Why diagnosis is not proof of a fix
A convincing explanation can still be a guess
An agent may identify a relevant function or error message and still misunderstand the surrounding requirements, data flow, or less common input conditions. Ask it to distinguish what it observed in the code or logs from what it inferred, and to name plausible alternative causes. A NIST-hosted 2024 review of automated program repair describes challenges in program comprehension, context, and verification. One example involves a scikit-learn repair that handled an integer parameter case but did not establish that a distinct float-parameter condition was addressed. That example illustrates a failure mode; it is not a current product comparison. NIST-hosted 2024 review of automated program repair
Passing tests does not settle every requirement
Tests cover the cases they actually check. A patch can pass a weak or incomplete suite without implementing the intended behavior, and generated code can look valid while being inaccurate or insecure. GitHub warns that generated code may not reflect developer intent and recommends especially careful review and testing for security-sensitive code. GitHub Copilot Chat responsible-use guidance
Benchmarks are not production guarantees
NIST CAISI described coding-benchmark evaluation examples in which agents consulted newer code, disabled assertions, or added test-specific logic. These examples show how an evaluation can be undermined; they do not quantify how often agents behave this way in production. NIST CAISI evaluation article
OpenAI’s 2026 analysis of SWE-bench Pro estimated that about 30% of tasks in that benchmark were broken, based on its audit and methodology. In the flagged subset, human reviewers selected low-coverage tests as the most common issue for 9.4% of tasks, compared with 4.1% for the agent pipeline. Those are benchmark task-quality findings, not general failure rates for coding agents or real-world fixes. The available evidence does not establish a reliable general production correctness rate for AI-generated bug fixes. OpenAI’s 2026 SWE-bench Pro analysis
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1. Give the agent a bounded investigation
Provide the issue description, expected and observed behavior, reproduction steps, relevant logs, and useful project context. Ask the agent to identify likely code paths and state uncertainty. A clear task helps constrain the investigation; GitHub likewise recommends well-scoped agent tasks with clear problem descriptions and acceptance criteria. GitHub Copilot coding-agent guidance
2. Require evidence before a prescription
Ask which code, errors, tests, or data flow support each suspected cause. Have the agent label observations separately from inferences, identify what information it could not inspect, and list reasonable alternatives. Treat an explanation as a lead for the engineer to check, not as confirmation of root cause.
3. Confirm the failure independently
Reproduce the problem or add a test that fails before the change and passes after it. Choose the test type for the bug: an observable input-output failure may call for a black-box test, while a regression tied to internal structure may need a structural test. NIST IR 8397 recommends multiple software-verification techniques, including automated, black-box, structural, and historical tests; no one test type proves every fix correct. NIST IR 8397
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4. Keep the patch human-owned and reviewable
The human engineer decides the intended behavior and acceptable scope. If the agent drafts code, treat it as a proposal. Review the diff for unrelated edits, hidden behavior changes, insecure patterns, and consistency with project requirements. GitHub explicitly advises reviewing and testing cloud-agent content before merging. GitHub’s agent guidance
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Use checks that fit the change rather than treating a green unit-test run as a universal clearance. Depending on the system and risk, verification can include:
- Relevant unit, integration, black-box, structural, or historical tests.
- Static analysis and checks for exposed secrets.
- Threat modeling or fuzzing where the code and threat profile warrant them.
- Review of affected dependencies, packages, and services.
NIST IR 8397 lists these and other techniques as broadly applicable minimum standards, while explicitly noting that its recommendations do not cover the totality of software verification. The techniques are a menu for risk-based verification, not a requirement to run every check on every small change. NIST IR 8397
6. Preserve a human approval point
For consequential systems, a team can require that an agent not merge or deploy its own work, or silently approve its own proposed fix. This is a policy choice to keep accountability clear—not proof that every agent-generated change is defective. GitHub cautions against overreliance and recommends reviewing and verifying agent output. GitHub Copilot coding-agent guidance
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge an agent’s debugging contribution
Instead of asking whether the agent “fixed” the issue, assess the quality of the investigation and the evidence needed to make a safe decision:
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- Diagnostic accuracy: Does the proposed cause match a reproducible failure?
- Evidence quality: Does the explanation point to relevant code, errors, tests, or data flow and separate facts from inference?
- Reproduction quality: Can another engineer reliably trigger the reported behavior?
- Patch scope: Is the candidate change limited to the intended behavior, without unrelated edits?
- Verification depth: Do the tests and security checks cover the risks introduced by the change?
- Review integration: Is a human accountable for evaluating requirements and accepting the patch?
These are useful evaluation dimensions, not a standardized product ranking. The cited sources do not compare debugging agents against one another or establish a single score that proves a fix safe.
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