System for Bringing AI Agent Actions into One Approval Centre

The problem

In day-to-day operations, agent drafts, the authority matrix and rules are held in separate records and files, preventing the team from reviewing the same case in a single view.

As a result, the link to source evidence can be lost while preparing approval, rejection and rollback queues, leading to repeated checks and uncertainty about the basis for the decision.

Incomplete automation may overlook a high-impact or chained action, while poorly controlled automation risks agents exceeding their authority.

What we set out to improve

• Time required to prepare approval, rejection and rollback queues for review
• Correct routing of high-impact or chained actions to the appropriate specialist queue
• Recording of recommendations corrected or rejected by the action owner

How the system works

1. 01 Retrieve agent draft, authority matrix and rule data from an authorised source
2. 02 Verify the source system, record identifier and recency information
3. 03 Transform the fields into the target process schema
4. 04 Prepare approval, rejection and rollback queue drafts
5. 05 Compare the drafts with business rules and source records
6. 06 Check for exceptions involving high-impact or chained actions
7. 07 Obtain the action owner's approval, correction or rejection
8. 08 Write the input, recommendation, changes and final decision to the audit log

Methods we used

• Source-traceable data extraction from agent drafts, the authority matrix and rules
• Structured output that restricts approval, rejection and rollback queue fields through business rules
• An exception gate that separates high-impact or chained actions
• A queue that shows the source and recommendation together for the action owner
• Duplicate-processing controls and rollback records to mitigate the risk of agents exceeding their authority

Where people stay involved

Data and security

Only the fields required for the task are processed from agent drafts, the authority matrix and rules. Source-system permissions remain in force, and personal or commercially sensitive data is minimised before being provided to the model. Every read, recommendation, human amendment and write to the target system is logged.

Who this suits

Frequently asked questions