Verification Agent: How It Works and Where It Fails

Verification agents independently assess another system’s output by comparing a predefined claim with externally collected evidence and returning an auditable pass, fail, or inconclusive verdict. The guide argues that as AI systems generate many plausible solutions cheaply, reliably selecting or validating correct ones has become a larger challenge, illustrated by the gap between the likelihood that a correct answer appears among samples and the accuracy of common selection methods. It describes five main approaches—self-verification, separate verifier models, runtime policy monitoring, execution-grounded checks, and formal proof—whose trade-offs center on independence, cost, coverage, and the consequences of failure. Effective verification signals should be scalable, faithful to real user intent, and robust against optimization, while common weaknesses include shared blind spots, proxy drift, stale passing results, and source-code-only checks that cannot detect broken rendered interfaces. The guide recommends using independent evidence such as browser execution, enforcing runtime policies for agent actions, retaining artifacts for review, rotating evaluation scenarios, and continually testing verifiers themselves against fixed cases with known outcomes.

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