Seven Controls to Put in Place Before an AI Agent Gets Production Access

When deploying an AI agent with real-world capabilities, multiple safeguards must be put in place. First, distinguish between reading data, recommending actions, and actually executing them. This three-step process provides checkpoints. Next, assign each tool its own narrow set of permissions. Rather than one broad credential, define which resources it can access, which operations it can perform, its duration, and the environment it applies to.

Add a budget for actions, limiting the maximum records changed, messages sent, cost, retries, and external systems interacted with per run. Approvals should occur at the moment of commitment, showing the exact outcome rather than vague "continue" options. External content must be treated as untrusted input and parsed as data, not defining the agent's goals or permissions.

Log decisions and effects separately, recording task ID, tool, target, approval reference, before/after state, and provider responses. Include observability alerts for unusual tool usage, failures, and out-of-hour actions. Implement an independent stop mechanism that can revoke credentials, disable execution, or halt the workflow.

Before granting live access, test scenarios like conflicting instructions, wrong recipients, failed external services, duplicate deliveries, expired approvals, budget overruns, and the activation of the stop mechanism. Ensure the checklist includes a tool allowlist, short-lived least-privilege credentials, separate read/write paths, explicit approvals for high-impact actions, action and cost limits, idempotency protection, tamper-resistant logs, monitoring, alerts, and a credential-level stop mechanism.

After extensive testing, gradually grant the agent more authority, always observing its behavior under real conditions.