AgentDesk Overcomes LLM Self-Review Bias with Dual Adversarial Validation

LLM-based agents suffer from systematic approval bias when self-reviewing outputs—reviewers and generators share blind spots that lead to correlated failures. AgentDesk solves this by deploying two independent adversarial reviewers that must reach dual consensus before passing, significantly raising confidence for production deployments of code generation, content QA, and data extraction workflows.