Why AI Agent Outputs Need Adversarial Review (and How to Add It in One API Call)

The Problem: Agents Grading Their Own Homework If you’re running LLM agents in production — whether with LangChain, CrewAI, or custom pipelines — you’ve probably built some kind of output validation. Maybe a second LLM call checks the first one’s work. Maybe you parse for structural issues. Here’s what I kept finding: LLM-based self-review has a systematic leniency bias. When you prompt an LLM to review output from another LLM (or itself), it overwhelmingly approves. The reviewer and generator share similar blind spots — they fail in correlated ways. This matters when your agent writes code that gets deployed, generates customer-facing content, or makes decisions affecting downstream systems. The Approach: Adversarial Review with Dual Consensus AgentDesk provides two interfaces for adding adversarial review: MCP Server (open source, MIT) — review-only. Pass in any content, get structured quality feedback. Runs locally with your own API key. Hosted REST API — generate + review + auto-fi