Enterprise operators deploying autonomous agents lack real-time mechanisms to detect behavioral anomalies, enforce policy boundaries, or halt execution mid-run. Governance frameworks assume predictable, pre-approachable behavior, but agents acting across sessions, executing code, and accessing external systems produce emergent behaviors that static controls cannot anticipate. No platform-scale layer exists to observe, score, and intervene in live agent execution across heterogeneous frameworks.

Enterprise teams deploying autonomous agents have zero runtime visibility into emergent behavior and no way to enforce policy or halt execution mid-run across heterogeneous frameworks.

Platform engineering and AI ops leads at enterprises running autonomous agents (e.g., via LangGraph, CrewAI, AutoGen, custom frameworks) in production with compliance or safety requirements.

Enterprises are pausing agent deployments specifically because they can't explain or control runtime behavior to compliance and security teams — this is a literal deployment blocker, and adjacent APM/observability budgets (Datadog, Splunk) prove willingness to pay for runtime visibility.

Ship a lightweight sidecar SDK that intercepts agent tool calls, LLM requests, and state transitions via OpenTelemetry-style instrumentation; MVP dashboard shows live execution traces, anomaly scores (deviation from declared intent), and a manual/automated kill-switch — start with LangGraph and CrewAI adapters.

AI observability and governance is a subset of the $40B+ observability market, with dedicated agent-ops tooling estimated at $2-5B within 3 years as enterprise agent adoption scales.