An enterprise AI agent needs more than permission to use a tool. It needs a defined scope, a person accountable for its decisions, and a record of what happened. Those controls become essential when the agent can change a customer record, send a message, or propose a financial entry.
Our starting point in Conduit is simple: the agent prepares the work; a named person approves consequential actions. That is the beginning of an operating model, not a guarantee that every decision will be correct.
What a useful approval must show
An approval button is only useful if the reviewer can understand what they are authorizing. A proposal should show:
- The proposed action and the system it will affect.
- The source evidence, including missing or conflicting information.
- The rule or reasoning behind the recommendation.
- The before-and-after values, where applicable.
- The consequence of approving, rejecting, or escalating it.
For example, a customer-record update should display the existing value, the proposed replacement, and the evidence supporting the change. A reviewer should not need to reconstruct the agent's entire run to make that decision.
Approval must also be tied to the action that executes. If the underlying record changes after review, recheck it before applying the proposal. Otherwise, a correct approval can authorize an outdated change.
Keep an audit trail that explains the action
Record the relevant inputs, workflow and model versions, proposal, approver, execution result, and any subsequent correction. Protect the record from alteration and apply access and retention rules to the sensitive information it contains.
Logs support investigation; they do not replace access controls, testing, or monitoring. NIST's AI Risk Management Framework treats governance as a continuing responsibility throughout a system's lifecycle. An approval queue is one part of that responsibility.
Increase autonomy by action type
A history of approvals is evidence to examine, not an automatic license to remove oversight. Before enabling automatic execution, the process owner should define eligible cases, value limits, exception thresholds, and the conditions that restore human review.
Start with a narrow, well-understood action. Test it against representative cases, including failures, then monitor the results. High-impact or ambiguous actions can remain subject to approval even when routine work is automated.
Not every action is reversible. An external email cannot reliably be recalled; a posted entry may require a compensating entry. Design the recovery path before enabling the write.
Start with one accountable workflow
Name the owner, map the evidence a reviewer needs, and agree on what stops execution. Bank reconciliation illustrates how this can work in a bounded finance process.
To assess the controls for your first workflow, talk with our team.
