AI governance insight

The control gap in agentic automation.

The harder question is not whether an agent can act, but when it must stop, escalate and show its work.

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Pegasus editorial team · Reviewed 21 July 2026

Who this helps

Built around a buying situation.

Operations and AI governance leaders
Product and risk owners
Teams piloting AI agents

Useful when

The trigger is already visible.

An agent can progress work without a clear stopping rule
Low-confidence output is not consistently escalated
The audit record shows activity but not accountable approval

The position

Agency changes the control question.

When software can select tools, sequence tasks and advance a workflow, control cannot rely on reviewing the final answer alone. Teams need explicit consequence classes, authorised actions, confidence and validation rules, human approval thresholds, safe failure and reconstructable evidence.

A defined scope and accountable decision owners

Controls matched to the real operating context

Evidence that can be reviewed, tested and improved

Scope

What the work includes.

Every engagement is scoped around authority, operating context, available evidence and the decision that must be made.

Define consequence

Classify actions by reversibility, consequence, data sensitivity and external commitment.

Design approval and exception routes

Define where the workflow must pause, what evidence a reviewer receives and who can approve or override.

Measure evidence and drift

Monitor exceptions, false confidence, drift, rework and near misses to improve both model and process controls.

Method

A controlled route from exposure to evidence.

Decision points, responsibilities and verification stay visible from the start.

Discover

Define the exposure, context, evidence, owners and decision that matters.

Design

Select proportionate controls and make responsibilities and hold points visible.

Implement

Embed the agreed system into the real operating workflow.

Verify

Test whether controls work and whether the evidence supports the conclusion.

Improve

Close actions, learn from exceptions and strengthen the system over time.

Outputs

Evidence designed for use.

Scope and evidence map

Prioritised findings and action ownership

Practical control or implementation pack

Verification record and next-step roadmap

Questions answered

Before you scope the work.

Is human-in-the-loop always sufficient?

No. A nominal approval step fails when the reviewer lacks time, context, authority or usable evidence. The quality and position of the review matter.

What is a safe stopping condition?

A defined state in which the workflow does not continue automatically—for example missing required evidence, conflicting data, low confidence, policy exception or a consequential external commitment.

Start the conversation

Bring the exposure. Leave with a controlled route forward.

A first conversation is used to understand context, authority and the decision required—not to force a predetermined package.