Definition
Destructive action gating
Destructive action gating is the practice of classifying agent actions by reversibility and requiring explicit approval for the irreversible ones — delete, bulk-archive, bulk-update, external sends — while letting safe, read-mostly actions run freely. It is the operational core of agent governance: the boundary is drawn around the action, so no amount of clever prompting lets an agent quietly do something it can't undo.
Key takeaways
- Destructive action gating requires explicit approval for irreversible agent actions while letting safe ones run freely.
- The boundary is drawn around the action by reversibility — delete and bulk-edit gated, read-mostly work inline.
- Robust gating attaches to the tool's classification, not the prompt, so it can't be reasoned around.
- Planoda gates destructive tools via a broker fed by the tool registry, recording every proposal and approval.
Not all agent actions carry the same risk. Reading the backlog, drafting a comment, or proposing a re-prioritization are reversible and low-stakes; deleting issues, archiving a project, or emailing a customer are not. Gating separates the two: each tool is marked destructive or safe, and only the destructive set requires a checkpoint. This keeps the agent fast where speed is harmless and careful exactly where carelessness would be expensive.
The principle that makes gating robust is that it attaches to the action, not the request. If the gate lived in the prompt ('please confirm before deleting'), a model could rationalize its way past it; when the gate lives in the tool's classification and is enforced by a broker, the destructive call simply cannot execute without approval. Auto-approval policies can still let trusted operations through within bounds, but the decision is always recorded.
Planoda gates every destructive agent tool through its propose/approve broker, sourcing the destructive flag from the central tool registry and writing each proposal and approval to the audit trail — so an agent's irreversible reach is bounded by policy, and the record of what it tried survives the 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.
- Agent broker (propose/approve broker)An agent broker is the runtime component that sits between an AI agent and the actions it wants to take, deciding per call whether to execute inline, require human approval, or refuse. It is where propose/approve governance is actually enforced — the gate is code in the request path, not a guideline in a prompt — so an agent literally cannot run a destructive tool without passing through it.
- Tool registryA tool registry is the single, authoritative catalog of the actions an AI agent may call, with each tool's name, typed arguments, and risk classification declared in one place. It is the source of truth a governance broker consults to decide whether a call is safe to run or must be gated — so capability and destructiveness are properties of the registry, not scattered per-caller decisions.
- Agent governanceAgent 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.
- Audit TrailAn audit trail is an append-only, time-ordered record of who did what, when, and to which object across a system. Every create, edit, delete, and approval is logged immutably, so any state can be traced back to the actions that produced it. Audit trails underpin accountability, debugging, compliance, and — increasingly — oversight of what AI agents do.