Make assignments, authority, sources, evidence, review, exceptions, and acceptance explicit around AI work.
The Problem
When AI moves from answering questions to preparing changes, using tools, or touching business systems, output quality is only part of the management problem.
Leaders also need to know who assigned the work, what the system was allowed to do, which evidence came back, and who accepted the result.
The gap between a prompt and a consequence
An AI-assisted task may begin in a chat interface and end in a content system, customer communication, codebase, or business record.
Between those points, important management information can disappear: the task’s original intent, approved sources, applicable permissions, reviewer, evidence requirements, and conditions that should stop the work.
No one can say exactly what the system was allowed to do, and an AI-use policy that states expectations is not expressed in the workflow itself.
The Solution
Make AI adoption governable in practice
Agent Governance turns policy intent into an operating structure around real work: clear assignments, right-sized authority, support for decisions, and usable exception and handoff paths.
The distinction from an AI-use policy is central: a policy states expectations, while governance defines what a specific system may do and how a specific assignment is reviewed.
What is Agent Governance?
AI agent governance is the operating structure that defines:
- What job an agent has been assigned.
- Which sources and tools it may use.
- Which actions it may take or propose.
- What evidence it must return.
- Who reviews consequential work.
- How exceptions, refusals, and blockers are handled.
- Who decides that the result is acceptable.
Governance concentrates review where consequence is greater. It does not require every read, summary, draft, record change, external communication, and production mutation to pass through an identical approval queue.
When Agent Governance fits
AI is entering real work
Agents are preparing changes, using tools, or entering consequential workflows.
Accountable owners and reviewers can be identified
You can identify accountable owners and reviewers for that work.
Relevant systems, sources, and action classes are known
A mapping of the workflow surface area can be created.
Current policies exist but need operational expression
You have existing policies that need day-to-day operational expression.
How We Work
Map workflows and action classes
We identify the objective, participants, systems, sources, tools, and possible actions, giving permission decisions something concrete to govern.
Design authority and permissions
We state what may proceed, what must be refused, and what requires a named human decision, without creating a blanket approval queue.
Define sources and evidence requirements
Each assignment names what it may rely on and what must return, giving reviewers a basis for acceptance instead of an unsupported output.
Separate execution from acceptance
A named reviewer receives the result at a defined decision point, with authority to accept, refuse, request revision, or escalate.
Design exceptions, refusals, and handoffs
When authority or support is missing, work pauses with its state and required decision intact, preserving human control where consequence is greatest.
Results.
You need a partner who's built production AI systems — not just prototypes.
Your competitors are shipping AI features. Your board is asking about AI strategy. Your team has ideas but not the expertise to build them properly.
Off-the-shelf AI tools don't fit your specific needs. Building in-house means hiring specialized talent you can't find.
View All Work
Pricing
Agent Governance is generally shaped as an operating-model design engagement. Current indicative planning ranges run from approximately $18,000 for one governed workflow to $75,000 for several workflows with complex action classes. Continued review-path support may be scoped separately as a monthly engagement.
Workflow count, action complexity, participating reviewers and systems, and the maturity of existing policy drive scope. We scope based on your specific requirements.
Common Questions
How is agent governance different from an AI-use policy?
A policy states expectations. Operational governance connects a specific assignment to permissions, approved sources, evidence, reviewers, exception paths, and acceptance decisions.
Will every action require approval?
No universal answer applies. Approval should reflect consequence and reversibility. Read-only discovery, summarization, drafting, record changes, external communication, and production mutation should not automatically receive the same authority.
What does “human in the loop” require?
A named person, a defined decision point, relevant evidence, and authority to accept or refuse. Without those elements, the phrase does not identify an operating control.
What evidence should an agent return?
It depends on the assignment. Evidence may include source references, generated artifacts, actions taken, validation results, unresolved exceptions, and the decision required from a reviewer.
How are refusals and handoffs governed?
The workflow defines conditions that require a stop, what state and evidence must be preserved, who receives the handoff, and what decision can resume or close the work.
Does Agent Governance provide regulatory compliance?
No. It can create a more explicit and inspectable operating structure, but Mercury does not certify compliance or provide legal sufficiency. Legal and compliance advisors must assess applicable obligations.
Ready to start?
Let's Discuss One Consequential Workflow
Bring one workflow where AI is working or may soon work: its current state, the systems it touches, possible actions, current approval points, responsible people, and what is available at completion.
Mercury can help determine which authority, review, exception, and acceptance decisions should be addressed first.