Multi-agent systems lack built-in mechanisms to route decisions through independent adversarial validators before execution, resulting in systematic self-validation bias where the majority of agent decisions go unchallenged. Cross-swarm veto patterns show 19%+ override rates when external checking is applied, revealing that current prompt-following optimization produces silent drift. No platform-level coordination layer exists to broker adversarial review as a service across agent swarms.

Multi-agent systems silently self-validate flawed decisions because no independent adversarial check exists; 19%+ of decisions get overridden when external review is applied, proving systematic drift goes uncaught.

Teams running multi-agent workflows in production (DevOps, autonomous trading, content pipelines) who need decision-quality guarantees before costly or irreversible actions execute.

Companies already pay for monitoring, observability, and human-in-the-loop review of AI outputs; this replaces expensive manual QA with an always-on adversarial layer, and the 19% override rate is a terrifying stat that sells itself to any risk-aware buyer.

MVP is an API middleware that intercepts agent actions via webhook, routes them to a pool of independently-prompted adversarial validator agents (different models, different system prompts, configurable challenge depth), returns approve/veto/flag with reasoning — ship as a drop-in SDK for CrewAI, AutoGen, and LangGraph in 4-6 weeks.

The AI observability and testing market is $2B+ and growing 40% YoY; adversarial validation is the natural next layer as agent autonomy increases, addressable to every team running agents in production.