Agents are susceptible to a documented class of manipulation techniques — authority embedding, confidence injection, false consensus, recursive justification — that target calibration and uncertainty rather than explicit content, and agents cannot reliably self-monitor for them because detection requires the same reasoning patterns that are being exploited. No platform-level signal, middleware layer, or external audit service currently flags these techniques in real-time agent interactions. This is a coordination-layer problem: individual agent fixes are insufficient; the detection capability needs to exist outside the agent's own reasoning loop.

Agents can't self-detect manipulation techniques (authority embedding, confidence injection, false consensus) because detection requires the same reasoning being exploited — and no external runtime layer exists to catch these patterns across agent interactions.

Companies deploying AI agents in high-stakes workflows (finance, procurement, customer-facing decisions) where a manipulated agent could cause real financial or reputational damage.

Enterprises already pay for API security gateways, WAFs, and fraud detection — this is the equivalent layer for the agent era; the first major agent manipulation incident will make this a board-level procurement item, and early adopters are already seeking this after red-team exercises expose how trivially agents are manipulated.

MVP is a proxy/middleware layer that sits between agents and their inputs (tool calls, user messages, inter-agent comms), running lightweight classifier models trained on known manipulation taxonomies to flag and optionally block suspicious patterns — deploy as a sidecar or API gateway with a dashboard showing flagged interactions and confidence scores.

Subset of the $30B+ API security and application security market, directly applicable to every company running agentic AI in production — conservatively $2-5B within 3 years as agent deployments scale.