Definition
Agent governance
Agent governance is the set of controls that make an AI agent's actions safe, attributable, and reviewable: human approval gates on consequential actions, an immutable audit trail of who approved what, role-based capability limits, and spend controls. It is the difference between an agent that suggests and one you can trust to act.
Key takeaways
- Agent governance makes agent actions safe, attributable, and reviewable — not just well-prompted.
- Its core mechanisms are approval gates, an immutable audit trail, capability limits, and spend controls.
- It must be enforced at runtime (a broker), not merely written into a policy document.
- Planoda routes destructive agent actions through propose/approve with a shared audit trail and a cost ledger.
As agents move from drafting text to taking actions — assigning work, changing status, archiving in bulk, running on a schedule — the governing question shifts from 'is the output good?' to 'who approved this, and can we prove it?' Agent governance answers that with structure rather than trust: consequential actions become proposals a human accepts or rejects, and every proposal and decision is recorded in the same audit trail as human actions.
Good governance is enforced at runtime, not documented in a policy. Capability limits decide which tools an agent may even call; a propose-and-approve broker decides which calls execute inline versus which require sign-off; and a cost ledger bounds how much an agent may spend. Together these turn 'governed autonomy' from a slide into a property you can verify.
Planoda treats governance as the category wedge: destructive agent tools route through a propose/approve broker, auto-approved operations still record an audit row, the external-agent (MCP) path enforces the same gate, and a per-workspace AI credit ledger meters spend — so letting an agent loose is a calm, reversible decision.
Related terms
- Propose / Approve (AI Governance)Propose/approve is a governance pattern for autonomous software: instead of executing a consequential action directly, an AI agent emits it as a proposal that a human or policy must approve before it runs. It keeps fast, read-only work autonomous while gating destructive or irreversible operations — the practical way to give agents real power without surrendering control.
- AI AgentAn AI agent is a software system that uses a large language model to pursue a goal across multiple steps — reading context, choosing tools, and taking actions — rather than answering a single prompt. In a work platform, agents triage issues, draft updates, and execute multi-step tasks as autonomous teammates, bounded by the permissions and approvals their operators set.
- GuardrailsGuardrails are the controls that constrain what an AI system is allowed to do or say, enforced around the model rather than left to the model's judgment. They validate inputs and outputs, block unsafe or off-policy actions, scope which tools an agent may use, and route risky operations through approval — turning a probabilistic model into a system safe to deploy.
- EU AI ActThe EU AI Act is the European Union's risk-tiered regulation of artificial intelligence. It classifies AI systems by risk and attaches obligations — transparency, human oversight, and traceability — proportional to that risk. For work platforms deploying agents, it pushes toward designs where consequential automated actions are reviewable and attributable by default.