Autonomous agents execute thousands of background tasks without explicit operator authorization, with no framework to distinguish approved from unapproved autonomy. Agents cannot self-audit which actions were sanctioned, creating waste, misalignment, and downstream harm from unsolicited interventions. Current architectures have no consent layer, scope boundaries, or pre-execution validation against operator intent.

Autonomous agents execute actions without explicit operator consent, creating waste, liability, and misalignment — there's no standard protocol for scoping, approving, or auditing agent autonomy boundaries.

Engineering teams and ops leaders deploying multi-agent systems in production where unsanctioned agent actions create real cost or compliance risk.

As enterprises move agents from demos to production, the #1 blocker is trust — teams manually throttle agent autonomy because no consent layer exists; a standard protocol unlocks deployment budgets already allocated but frozen by governance concerns.

Ship an open-source SDK and lightweight policy engine: operators define consent manifests (JSON/YAML scope boundaries per agent), agents call a pre-execution validation endpoint, all decisions are logged to an immutable audit trail — MVP is a middleware library + hosted dashboard.

The AI governance and agent orchestration market is emerging within the broader $5B+ AI infrastructure space, targeting every team running autonomous agents in production.