Giving an AI Agent Access to Your Systems, Safely
An agent that reads is a research tool. An agent that writes is an employee with no judgement and no memory of consequences. Scope accordingly.
The moment an agent moves from answering questions to taking actions — sending an email, updating a record, issuing a refund — the risk profile changes completely. A wrong answer is embarrassing; a wrong action is a customer receiving something you did not authorise, at machine speed, several hundred times.
Scope credentials to the task
- Give the agent its own identity, never a shared admin account. You cannot audit what you cannot attribute.
- Grant the narrowest permission set that completes the job. An agent that drafts replies needs read access and draft creation, not send.
- Separate read and write paths. Most agent value comes from reading; writing is where the damage lives, and the two rarely need the same breadth.
- Time-bound and rotate credentials as you would for any service account.
Approval gates that survive contact with users
Full autonomy is rarely the right first setting, and full approval is quickly ignored — people click through everything after the first week. Gate by consequence instead: irreversible actions and anything above a value threshold require confirmation; reversible, low-value actions run freely. That distinction keeps approvals meaningful, which is the only way they stay effective.
Containing a bad decision
- Rate limits on every action. An agent stuck in a loop should send five emails, not five thousand.
- A kill switch that a non-technical person can operate, tested before it is needed.
- Reversibility by design: soft deletes, drafts rather than sends, staged rather than committed changes.
- Alerts on unusual patterns — volume spikes, actions outside working hours, repeated failures.
The audit trail
Log the request, the data retrieved, the model's output, the action executed and the authority under which it ran. When something goes wrong — and eventually it will — this is the difference between a five-minute explanation and a week of guessing. It is also what an insurer or a regulator will ask for first. This is the ground our AI integration work starts from: fifty real cases, then the smallest system that clears them.
- 1 identity per agent
- consequence the basis for approval gates
- 1 kill switch anyone can use
Frequently asked questions
Should agents have production database access?
Read access through a scoped view, occasionally. Direct write access to production, essentially never — route writes through an API with validation, so the same rules that protect you from a buggy script protect you from a confused model.
How do we test an agent safely?
A staging environment with realistic but synthetic data, then a limited production pilot with approval on every action. Measure the agreement rate between what the agent proposed and what the human approved before removing any friction.
More on this topic: Artificial Intelligence.
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