AI Agent Security describes the service and positioning; this guide is the hands-on implementation reference for a team actually building an agent today.
1. Bound tool-call permissions explicitly
Never let an agent call a tool "because the model decided to." Every tool an agent can invoke should have an explicit allow-list, scoped credentials (not the same API key a human admin uses), and rate limits independent of the model's own judgment.
2. Gate consequential actions behind human approval
Define what counts as "consequential" for your system — sending money, deleting data, sending external communications, changing permissions — and require explicit human sign-off before the agent executes it, not just a log entry after the fact.
3. Sandbox execution
Where an agent executes code or shell commands, run it in an isolated environment with no access to production credentials or the broader network by default. Assume generated code can be wrong or hostile.
4. Validate inputs and outputs against a schema
Constrain what a tool call can contain (JSON schema validation on arguments) and what a response is allowed to trigger downstream — don't let free-form model output flow directly into a privileged action without a validation layer in between.
5. Audit trajectories, not just final outputs
Log the full sequence of tool calls and intermediate reasoning an agent took to reach an action, not just the final result — this is what makes an incident actually investigable after the fact.