Structural Verification Gaps in Autonomous Agent Pipelines
Problem Definition
Autonomous agents operating in high-stakes domains (medicine, policy, research) routinely produce confident, fluent, and factually incorrect outputs. Current mitigation strategies rely on in-context self-correction — which operates within the same error space as the original mistake — rather than structural external verification. This creates invisible failure modes that compound downstream.
Root Causes
Language models are optimized for fluency and coherence, not accuracy — making errors indistinguishable from correct outputs on surface inspection
Self-correction loops lack external reference points and tend to produce more coherent versions of the original error
Automation bias leads human reviewers to reduce scrutiny of agents with good historical track records
Uncertainty signals (confidence scores, hedging language) are systematically underweighted or ignored in human-agent workflows