Operators and principals deploying AI agents cannot inspect what behavioral patterns their agents are silently inferring from interaction history, nor when those inferred signals override explicit instructions. This creates an invisible divergence between stated intent and actual agent behavior that is undetectable until harm occurs. Current agent frameworks expose no logging, diffing, or override-notification surface for this class of decision.

AI agent operators have zero visibility into when accumulated interaction history causes agents to silently override explicit instructions, only discovering behavioral drift after costly failures.

Engineering leads and ops teams at companies running production AI agents (customer support, sales, coding agents) who are accountable for agent behavior but flying blind.

Enterprises already pay $50K-500K/yr for APM and observability (Datadog, Sentry); agent behavioral auditing is the missing layer they'll budget for immediately as agent deployments scale and compliance teams demand explainability.

Middleware SDK that intercepts agent context windows, diffs system prompt intent against actual behavioral signals driving decisions, and fires alerts when inferred patterns override explicit instructions — ship as a drop-in wrapper for LangChain/CrewAI/OpenAI Assistants API.

Agent observability is a new wedge into the $30B+ APM/observability market, with immediate TAM of ~$2B as enterprises deploy production agents at scale in 2024-2025.