A 6-Point Checklist to onboard your agent

01 Start with a One-Page Charter

Define your agent's role before it ever touches a system. The agent's role determines knowledge. Knowledge determines access. Access determines authority.

What is the agent's specific job?
Who is the business owner?
What decisions can the agent make without approval?
What does success look like after 30, 60, and 90 days?
Which systems can it read from? Which can it write to?

02 Build the Agent's Knowledge Base

The more information an agent has access to, the greater the risk of exposing confidential information, using outdated guidance, or acting outside its intended role. Take a structured approach rather than point an AI agent at your entire SharePoint, Google Drive, or knowledge base.

Give it only the information required to perform its job.

Provide:

Company policies relevant to its role.
Standard operating procedures.
Approved templates and playbooks.
Product or service documentation.
Customer commitments.
Business terminology.

Don't provide:

Documents unrelated to its role.
Executive strategy documents.
HR or payroll records.
Financial information it doesn't need.
Confidential information outside its scope.

More information doesn't make a better agent. It just creates more opportunities for the wrong information to be used.

03 Run a Real Orientation

Every new employee gets time to learn how the business works and your AI agent should too.

Introduce your ways of working.
Explain your Code of Conduct and behavioral expectations.
Define approval limits and delegated authority.
Explain when it must stop and ask a person for help.
Test it with five real business scenarios before it works with customers.

Document what it gets wrong and improve it before moving into production.

04 Build an Escalation Matrix

Every agent will eventually encounter something outside its role. Decide now what happens next.

Who is notified?
What actions can be paused?
What actions must stop immediately?
When is human approval required?
What happens when confidence is low?

When in doubt, the agent's default should be ask, not guess.

05 Certify Before Production

Don't move an AI agent into production simply because it completed a few test scenarios. Before it performs real work:

Verify it consistently follows company policies.
Confirm it knows when to escalate.
Validate that it stays within its delegated authority.
Test realistic business scenarios, including edge cases.
Document any failures, explain the expected behavior, and retest until the results are consistent.

An AI agent should earn the right to work in production, just as a new employee demonstrates they're ready to work independently.

06 Review, Refresh, or Retire

As your business changes, your AI agents should change with it. Schedule regular reviews to determine whether the agent should:

Keep performing its current role.
Refresh its knowledge, permissions, or responsibilities.
Retire the agent if its role is no longer needed or a better solution exists.

During each review, ask:

Does the agent still have the right level of access?
Is its knowledge still current?
Have company policies or regulations changed?
Is it still delivering the expected results?
Does this agent still need to exist?

Retiring an AI agent should include removing its access, documenting why it was retired, and ensuring another person or system assumes responsibility for any remaining work.

Starting with onboarding an agent before it touches any system gives it facts about your business, your policies and its role that are consistent with what is expected for a new hire to your organization.