When AI Agents Argue: Structured Dissent Patterns for Production Reliability // Phil Stafford

Single-agent LLM systems fail silently in production - they're confidently wrong at scale with no mechanism for self-correction. We've deployed a multi-agent orchestration pattern called ""structured dissent"" where believer, skeptic, and neutral agents debate decisions before consensus. This isn't theoretical - we'll show production deployment patterns, cost/performance tradeoffs, and measurable reliability improvements. You'll learn when multi-agent architectures justify the overhead, how to orchestrate adversarial agents effectively, and operational patterns for monitoring agent reasoning quality in production. Our first deployment of the debate swarm revolves around MCP servers - we use a security swarm specially built for MCP servers to analyze findings from open source security tools. This provides more nuanced reasoning and gives a confidence score to evaluate the security of unknown MCP tools.