Putting an LLM in front of citizen data is not a model problem. It is a trust problem. These are the patterns that got our deployments approved.
Start with the audit trail
Every agent action (retrieval, draft, decision) is logged with its inputs, outputs and the person who approved it. Reviewers care less about accuracy claims than about being able to replay exactly what happened.
Keep a human in the loop
- Agents draft, officers approve
- Confidence thresholds route edge cases to people
- Every override becomes feedback for the next iteration
The fastest way to get AI approved is to make it easy to say no to.
Data stays where it belongs
Region-locked hosting, retrieval scoped by role, and no personal data sent to third-party models without redaction.
agent.run(task, {
scope: user.role,
redact: ["nic", "phone"],
requireApproval: confidence < 0.85
})What I would do differently
Bring compliance officers into the first sprint, not the last. Their questions shape the architecture far more cheaply early on.
