Bounded autonomy is an operating model where AI agents work independently within defined limits, with governance that steps in only when an action requires review or violates policy.
Full autonomy is risky — agents can take harmful, non-compliant, or unintended actions without oversight. Full supervision is expensive — human review of each agent action removes much of the efficiency gain that agents provide. Organizations need a middle ground that balances agent productivity with governance accountability.
Bounded autonomy addresses this by defining clear operational limits. Agents operate freely within those limits, and governance produces explicit decisions when actions cross defined thresholds. Human review is directed to the actions that policy routes for review rather than to all actions; it is not removed.
Without bounded autonomy, organizations face a binary choice: trust agents completely or supervise them entirely. With it, governance is proportional — applied where it matters, producing reviewable records at each governed decision point.
Manual approval requires human review for each action. Bounded autonomy allows agents to operate freely within defined limits, with governance intervening at policy boundaries.
Kill switches stop agents entirely. Bounded autonomy maintains agent operation while enforcing limits — escalating or restricting specific actions, not shutting down the agent.
Monitoring observes agent behavior without intervening. Bounded autonomy enforces customer-defined policies, producing explicit decisions that shape agent actions in real time.