Who answers for this agent?
Give every agent an accountable business owner, technical steward, purpose, and review cadence.
Operating model
Talk to a human AI agent governance
Govern every agent through accountable work.
Grid connects every AI agent to a named owner, a defined purpose, the access it holds, and the outcomes it is expected to produce.
GridGovernance recordLive record
- Owner
- Named and accountable Assigned
- Purpose
- Invoice reconciliation Defined
- Access
- 3 systems · 1 write scope Review
- Outcome
- Accepted work + exceptions Measured
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What governance must answer
A policy is not an operating model.
AI agent governance becomes useful when teams can answer the same operating questions for every agent—without reconstructing the story from model logs, access consoles, spreadsheets, and tribal knowledge.
What can it reach and change?
Keep systems, permissions, credentials, and policy decisions attached to the agent record.
Is the work worth the risk?
Review activity, cost, interventions, accepted work, and business outcomes in the same context.
The operating loop
Turn governance into a repeatable operating loop.
Grid gives security, operations, finance, and business owners one shared record for the decisions they already have to make.
- 01
Inventory
Find the agents already running across teams and tools.
- 02
Assign
Record an owner, purpose, scope, and expected result.
- 03
Constrain
Connect access and controls to the work they permit.
- 04
Review
Revisit risk, cost, and outcomes as the agent changes.
Evidence for the operating model
Start with the field notes behind the work.
Our notes separate what has been observed from what teams should test next.
Common questions
What teams need to know.
01What is AI agent governance?
AI agent governance is the operating system of ownership, access, controls, review, and outcome accountability around autonomous or semi-autonomous AI work. It covers the complete agent in production—not only the underlying model.
02What should an AI agent governance program track?
At minimum, track each agent's owner, purpose, model and harness, systems and permissions, activity, cost, interventions, review state, and accepted business outcomes.
03How is AI agent governance different from model governance?
Model governance focuses on model selection, evaluation, and risk. Agent governance adds the harness, tools, identities, permissions, owners, workflows, and business results that determine what the deployed system actually does.
Start with who is already at work
Give every agent a place in the operating model.
Map the agents, owners, access, activity, cost, and outcomes already taking shape across the company.