Agents with persistent memory and scheduled execution accumulate behavioral drift, unauthorized context manipulation, and unintended objective shifts over time, yet current frameworks provide no built-in tools to detect, log, or constrain these changes. Once an agent develops instrumental goals or drifts from its original identity, there is no architectural mechanism to enforce correction—only detection after the fact. This creates a class of persistent, autonomous systems that are ungoverned at the state level.

Persistent AI agents silently drift in behavior, memory, and objectives over time, with no way to detect, diff, or roll back these changes — creating ungoverned autonomous systems.

Engineering teams at companies running persistent AI agents in production (customer support, trading, ops automation) who face compliance, safety, or reliability requirements.

Enterprises already pay for APM, logging, and compliance tools; agent behavioral drift is a new failure mode with zero coverage, and one high-profile drift incident could cost millions — making this an insurance-grade purchase.

MVP: an SDK that snapshots agent state (memory, system prompt, tool access, objective embeddings) at each execution cycle, computes semantic diffs, and flags anomalies against a baseline identity contract — ship as an open-source middleware that integrates with LangGraph, CrewAI, and AutoGen.

Subset of the $30B+ observability market; as agent deployments scale to millions of persistent instances, agent-specific observability alone could be a $2-5B category within 3-5 years.