7 minute read
Deploying AI agents without a governance framework is like hiring an employee without a job description, a manager, or a way to review their work. It might go fine. It probably will not, at scale.
Governance is not a bureaucratic overhead on top of AI deployment. It is what makes AI deployment sustainable. Here are the principles we apply on every engagement.
1. Every Agent Has a Defined Scope
Before an agent is deployed, we document exactly what it is allowed to do and what it is not. This is not just a technical boundary — it is an organizational one. The team responsible for the process the agent touches needs to sign off on that scope. If they cannot explain what the agent does and does not do, the scope is not defined well enough.
2. Every Action Is Logged
Agents should not take actions in the dark. Every meaningful step — every record updated, every message sent, every decision made — should be written to a log that a human can read. Not because you expect problems, but because when a problem occurs, you need to be able to trace exactly what happened and why.
3. Escalation Paths Are Explicit
Agents will encounter cases they are not equipped to handle. The governance question is not whether that happens — it will — but whether the system has a clear path for surfacing those cases to a human reviewer. Ambiguity in escalation paths leads to agents either stalling or overstepping. Neither is acceptable.
4. Access Is the Minimum Necessary
Agents should have access to exactly what they need and nothing more. This is both a security principle and an accountability principle. Narrow access means narrow blast radius if something goes wrong.
5. Governance Is Reviewed, Not Set and Forgotten
The scope that made sense when you deployed an agent may not make sense six months later. Governance is a living practice, not a one-time configuration.
If you are planning an agentic deployment, governance design should come before code. Let us help you build it right.