Metaharness Framework Uses Multi-Agent Adversarial Reviews to Catch AI Reviewer Errors

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A proposed 'metaharness' system addresses a key flaw in AI-assisted code review: a single AI reviewer can generate confident but incorrect findings that block pull requests or erode team trust. The framework routes code changes to human reviewers when confidence falls below 0.7, costs exceed a set budget, or benchmark results are statistically ambiguous. Specialist and adversarial agents split review roles, with cross-examination used to challenge and filter unreliable findings. The routing logic is encoded explicitly in policy code rather than left to reviewer intuition, ensuring deterministic, auditable behavior. By default, only mechanical and graph-based validations are allowed to gate a pull request automatically, keeping human oversight central to uncertain cases.

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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ProgrammingDEV Community ·

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