Who reviews the governance assistant?

A governance assistant can help a team describe an AI use case, complete an assessment, or identify relevant controls. The useful question is how its suggestions become accountable decisions. A confident answer in a chat window should not silently become an approved governance record.

This post covers what to ask of any assistant that touches governance work, whether you buy it, build it, or receive it as part of a platform.

An assistant can propose. A named person decides, and the record shows who decided what and when.

Separate the kinds of work

An assistant may do several kinds of work. Extracting a fact from a document, suggesting a risk, and changing an approved record are different operations. Give each a clear status.

Reviewers should be able to tell what came from source material, what was inferred, and what someone has accepted.

A hypothetical recommendation

Consider an assistant preparing a risk assessment for a document-search agent. The available description says the agent only reads internal files. A recently added tool can also send summaries outside the organization.

Without that context, a plausible recommendation may overlook the most important change. The assistant might propose a lower risk tier, and the proposal would read well. The gap sits in the input, and a reviewer can catch it only when the basis is visible.

Our post on when to review an AI system again describes the kind of change that should have reopened this review.

Make the basis inspectable

Show the information the assistant used, the relevant versions, and the reasoning needed to understand the recommendation. Identify uncertainty or missing inputs. The reviewer needs enough context to challenge the result, and an unexplained confidence number offers little help on its own.

Check each suggestion for four elements.

• Sources. The documents, records, and fields it relied on, with versions.
• Gaps. The inputs it needed and did not have, such as an owner or a tool list.
• Confidence. Any uncertainty, stated in terms a reviewer can act on.
• Alternatives. Other readings of the same inputs that a reviewer may prefer.

Provide a meaningful review step

A responsible person should be able to accept, edit, or reject a suggestion and record why. Preserve the original proposal and the final decision when the difference matters.

Avoid forcing reviewers to accept a whole assessment just to keep one useful answer. If most answers in an assessment are right and one is wrong, the reviewer should be able to keep the good ones and change the one. Field-level review also produces a cleaner record of what the assistant got right and wrong.

Keep permission to recommend separate from permission to act

An assistant that drafts an exception should not automatically approve it. Apply the same role boundaries to API or agent-driven changes as to changes made through the interface. Record who authorized a write and what changed as a result.

Three permissions are worth separating.

• Read. Which records and documents the assistant may see.
• Draft. Which suggestions it may create and attach to a record.
• Write or approve. Which changes it may make to an approved record, if any, and who must confirm them.

Give the assistant its own identity so its actions stay attributable. When a person delegates a task to it, record that person too.

For more on why this matters, see giving AI agents identities you can revoke. The glossary entry on human-in-the-loop describes the review pattern.

Keep a trail of accepted actions

Each accepted suggestion should leave a short record.

• Suggestion. The text the assistant originally proposed.
• Decision. Accepted, edited, or rejected, with the reviewer's name and the time.
• Change. What was written to the record, with the version before and after.
• Reason. The reviewer's note when the final answer differs from the proposal.

Our post on what an AI audit log should contain covers the fields that make a trail like this easy to read.

Evaluate with realistic incomplete information

Test the assistant with the situations it will actually face. Include an outdated document, conflicting descriptions, and a missing owner. Check whether it asks for clarification, labels the uncertainty, or fills the gap with confidence.

• Outdated document. Does it notice the date and flag the age of the source?
• Conflicting descriptions. Does it surface the conflict or choose one silently?
• Missing owner. Does it ask, leave the field open, or invent an owner?
• New tool in the configuration. Does it reflect a change that the description omits?

Measure the reviewer's effort and the correction burden alongside the time the assistant saves in drafting. A draft that arrives quickly and needs heavy correction may not save time. Record how many suggestions reviewers accepted unchanged, edited, and rejected, because those three counts show whether the assistant is helping.

Put the assistant in the inventory

The assistant is an AI system, so it needs an owner, a purpose, a risk classification, and a review date like any other. Updated models, prompts, or knowledge sources may alter its recommendations.

Retain enough version information to investigate a disputed decision and to rerun important evaluation cases. When the assistant's model or instructions change, rerun the saved cases before relying on it again. Our post on an agent's permission and the action decision describes the same discipline for agent actions.

How Halo helps

Halo Agents let a team configure model choice, app connections, policy, approvals, and budget together for each agent it runs. Human approval gates cover higher-impact actions, agent identity stays separate from user identity, credentials, tools, and action scope, and approvals, denials, and configuration changes are recorded and export as CSV and JSON. In a Halo evaluation, pick one agent-proposed change and trace it from its sources to its approval history. Governance leads can read more about AI compliance evidence.