AI Agent Runtime Governance Checklist
A practical readiness check for organisations moving from AI chat to AI action. Use it to find control gaps before agents inherit access to tools, data, workflows, and internal systems.
Identify which tools and actions agents can use across business systems.
Classify actions as allow, review, or block before production scale.
Give security, platform, risk, and AI governance teams a shared control model.
- Which agent actions should be blocked outright, reviewed by a person, or allowed automatically?
- Where do tool permissions, data access, and business accountability need clearer ownership?
- What evidence will leaders need before expanding agent autonomy beyond pilot use?
Enter your details to receive the download.
Your details are submitted through the same Summit Guard contact workflow and recorded as a website lead for follow-up.
- The form records the request as a website lead so the team can see which checklist was requested.
- The confirmation screen provides the direct PDF download link immediately after submission.
- The checklist is designed as a discussion aid, not a substitute for formal policy, risk, privacy, or security review.
For a broader conversation, use the standard contact page.
Use the checklist before agent autonomy expands.
It is most useful when teams already have AI policy intent, but need a shared way to decide what agents may do in live workflows.
- Can the agent call external tools, trigger workflow steps, or change records without a human approving every action?
- Does the agent have access to customer, employee, financial, operational, or commercially sensitive data?
- Would a failed or inappropriate action create real business disruption, reputational risk, or manual rework?
- AI governance leaders deciding how much autonomy is acceptable
- Security and platform teams setting safe tool and data boundaries
- Risk, privacy, and delivery teams preparing agents for wider use
Move from policy intent to operating controls.
The checklist frames agent governance as a live operating question: what the agent can do, when a person must step in, and what evidence remains after each action.
Permission boundaries
Define the tools, records, and workflow steps an agent may use before it is connected to production systems.
Human review points
Separate low-risk actions from changes that need approval, escalation, or a manual handoff.
Runtime evidence
Capture decisions, prompts, tool calls, and outcomes in a form that business, platform, and governance teams can review.
Rollback paths
Agree how teams will pause an agent, reverse a failed action, and learn from incidents without slowing every safe use case.
A clearer runtime governance conversation.
The checklist is designed to turn vague agent risk concerns into practical runtime decisions: allow, review, or block.
- A short list of agent actions that should be allowed, reviewed, or blocked.
- Clear ownership prompts for tool access, data exposure, runtime logs, and escalation paths.
- A practical next-step view for pilots that need stronger controls before wider rollout.
Low-risk, reversible actions where permissions are narrow and evidence is captured automatically.
Material actions where a person should approve the agent recommendation before a workflow changes state.
Actions outside the agent mandate, involving sensitive access, irreversible change, or unclear accountability.
Keep the first conversation narrow.
The fastest way to make the checklist useful is to test one real agent action, then repeat the pattern as autonomy expands.
Pick one agent action
Start with a specific workflow step the agent may take, not a broad platform or policy statement.
Map the runtime decision
Decide whether that action should be allowed, reviewed, blocked, logged, or escalated.
Agree the next control
Turn the discussion into one practical improvement to permissions, evidence, review, or rollback.
Plain-English runtime governance prompts.
What is AI agent runtime governance?
AI agent runtime governance is the operating model for controlling what an AI agent can do while it is running, including tool access, approval points, logging, escalation, and rollback paths.
Who should use the checklist?
The checklist is for leaders and delivery teams preparing AI agents, copilots, or workflow automations that can access data, call tools, or take action across business systems.
Does the checklist replace an AI policy or risk review?
No. It is a practical prompt for finding runtime control gaps before autonomy expands. It should sit alongside existing AI governance, security, privacy, and risk processes.
Is this mainly an AI security checklist?
No. Security is one input, but the checklist is focused on runtime governance: permissions, accountability, review points, evidence, and rollback decisions for AI agents in live workflows.
Which agent action should we assess first?
Start with one action that combines tool access, business impact, and unclear accountability. The checklist works best when the first discussion is tied to a specific workflow step rather than a broad AI platform decision.
What evidence should teams keep from agent runs?
Keep enough runtime evidence to explain the agent instruction, tool call, permission used, human review decision where relevant, final outcome, and rollback or escalation path if the action goes wrong.
Request the checklist and use it in your next governance discussion.
Start with one workflow step, decide what should be allowed, reviewed, or blocked, and identify the next control improvement.