Action-Level Gating

Action-level gating is a security mechanism that intercepts every discrete action an AI agent attempts to perform, evaluates it against a defined policy, and either allows, denies, or escalates the action before execution occurs.

In Detail

Traditional approaches to AI agent safety operate at the session level or the prompt level. A user might be granted access to a tool, and from that point forward, every invocation of that tool is permitted. Action-level gating rejects this coarse model. Instead, it treats each individual action — every file write, every shell command, every network request — as an independent decision point subject to its own policy evaluation.

The mechanism follows a three-phase cycle: intercept, evaluate, resolve.

1. Intercept. When an AI agent attempts an action, the gating layer captures the action request before it reaches the underlying system. The action has not yet been executed. The agent's intent is known, but no side effect has occurred.

1. Evaluate. The intercepted action is compared against a set of policies. These policies specify conditions (action type, target path, arguments, context) and effects (allow, deny, or require approval). The policy engine processes the action and produces a verdict.

1. Resolve. Based on the verdict, the action is either executed, blocked, or held pending human approval. The outcome is logged to an audit trail regardless of the decision.

• Monitoring observes actions after they have been executed. It can detect problems but cannot prevent them. A monitoring system records that an agent deleted a production database; action-level gating prevents the deletion from occurring.

• Sandboxing restricts the environment in which an agent operates, limiting available resources or capabilities broadly. Sandboxing might prevent all file system access. Action-level gating permits file reads to safe directories while denying writes to sensitive paths — the control is granular, not binary.

Examples

• An AI coding agent attempts to execute rm -rf /. The gating layer intercepts the shell_exec action, evaluates it against a policy that denies destructive shell commands, and blocks the action. The agent receives a denial response.

• An AI assistant attempts to read a configuration file at ~/.ssh/config. The policy permits file_read actions on project directories but denies reads outside the project root. The action is denied.

• An AI agent attempts to write a test file to ./tests/unit/new-test.ts. The policy permits file_write actions within the tests/ directory. The action is allowed.

• An AI agent attempts to make an outbound HTTP request to an unknown domain. The policy requires human approval for network actions targeting domains not on the allowlist. The action is held until a human approves or denies it.

Related Concepts

• Deny-by-Default — The architectural principle that unpermitted actions are blocked, which underpins action-level gating.
• Policy Engine — The component that evaluates each intercepted action against rules.
• Human-in-the-Loop — The escalation path when a gating decision requires human judgment.
• AI Agent Action Types — The categories of actions that gating evaluates.
• Simulation Mode — A testing mode that logs gating decisions without enforcing them.

In SafeClaw

SafeClaw, by Authensor, implements action-level gating as its core mechanism. Every action an AI agent attempts — whether file_write, file_read, shell_exec, or network — passes through SafeClaw's local policy engine before execution. The evaluation runs locally with sub-millisecond latency, meaning gating does not meaningfully slow agent workflows.

SafeClaw's policy engine uses a deny-by-default architecture: any action not explicitly permitted by a policy rule is denied. Policies are defined as ordered rules, each specifying a condition and an effect (allow, deny, or require_approval). Evaluation follows a first-match-wins model.

Because SafeClaw runs locally with zero third-party dependencies and is written in TypeScript strict mode with 446 tests, the gating layer itself is not a source of supply chain risk. The control plane only receives action metadata — never the content of files or commands — preserving the confidentiality of agent operations.

SafeClaw works with Claude, OpenAI, and LangChain agents, and can be installed with npx @authensor/safeclaw. A free tier is available with 7-day renewable keys and no credit card required.