As AI agents take on more consequential work — modifying production code, merging pull requests, interacting with customers — the question of how to maintain accountability becomes critical. Governance answers: who authorized this action, what constraints applied, how was the outcome verified, and what recourse exists if something went wrong?
Governance controls typically include: role-based access (agents can only act within their defined scope), approval gates (certain actions require human sign-off before execution), budget limits (caps on compute, token spend, or wall-clock time), audit logs (immutable records of every agent action), and kill switches (the ability to stop a rogue agent immediately).
The regulatory dimension is emerging. The EU AI Act, US executive orders on AI, and sector-specific guidance are beginning to impose requirements on autonomous AI systems in regulated industries. Governance infrastructure built for operational reasons often satisfies these requirements as a side effect.