FileMaker Exception Reviewer demo.
A safe first agent template for FileMaker: find stale work, missing details, mismatched totals, and approval bottlenecks, then prepare a review queue before anything gets written back.
Sample exceptions the agent would package for humans.
This is deliberately boring. That is the point. Useful FileMaker agents should make operational risk visible, preserve the source fields checked, and hand humans an approval-ready queue.
| Record | Issue | Source Fields Checked | Proposed Next Action | Risk |
|---|---|---|---|---|
| JOB-1048 | Proof approval is 6 days stale | Status, Proof Sent Date, Customer Contact, Last Activity | Draft a follow-up email and move to manager review | Low |
| INV-2231 | Invoice marked sent, but no payment terms on record | Invoice Status, Terms, Customer Type, Balance Due | Flag billing admin before statement run | Medium |
| ORD-7782 | Shopify total does not match FileMaker order total | Order Total, Tax, Shipping, Discount, External Order ID | Create reconciliation task; block write-back until reviewed | High |
| CON-332 | Duplicate contact candidates share company and phone | Company, Phone, Email, Last Order Date, Owner | Prepare merge packet for human approval | Medium |
How the template runs
- Read trusted FileMaker report, export, or Data API snapshot
- Apply business rules and stale-work checks
- Prepare review queue with sources and proposed next action
- Wait for human approval, revision, or rejection
- Write back only after the approval log captures the decision
What gets logged
Record id, source fields checked, proposed action, risk note, reviewer, approval decision, timestamp, and final write-back result. That gives the business an audit trail instead of a black-box agent.
Why this is a strong first FileMaker agent pattern
Exception review is where AI inside FileMaker starts making practical sense. The agent is not inventing strategy. It is reading trusted records, finding stale work or mismatches, packaging the evidence, and handing a human a cleaner decision queue.
That keeps the workflow grounded in real tables, scripts, reports, and business rules. Teams get faster review without pretending the model should quietly rewrite orders, invoices, contacts, or customer communications on its own.
When the underlying FileMaker system is already brittle, the right first step is often a rescue or reliability audit before an exception agent goes live. A review queue cannot stay honest for long if the source report, script path, or integration inputs are already drifting.
Good first exception lanes
- Open jobs, quotes, or follow-ups that have gone stale without an owner response.
- Billing or statement issues that need a human check before customer-facing actions.
- Shopify, shipping, or accounting mismatches that should block automated write-back.
- Duplicate-contact or missing-data cases where the business needs review before merge or update.
What makes the proof believable
- Visible source fields, risk notes, and the exact proposed next action for each record.
- A review table or queue instead of silent updates against production records.
- Approval, rejection, edit, and defer paths with timestamps and reviewer identity.
- Clear escalation when rules conflict or the source data is incomplete.