Agentic AI Governance: A Practical Checklist for Australian Teams

AI agents that can take actions (send emails, update records, call APIs) need governance. Here is a practical checklist for Australian teams, written in plain language. No jargon, no fluff. Just the stuff that actually matters.

Define the scope. What can the agent do? List every action it can take. If the list is long, start with a narrower scope and expand later. We have seen this work well with Germonizer (a secure platform for biological threat monitoring) where every capability had to be explicit and auditable from the start. Same with Looper Insights: the AI summarisation and anomaly detection had clear boundaries because we defined them upfront.

Map the data. What can the agent read? What can it write? Ensure access aligns with role-based permissions. Do not give the agent more access than a human in the same role would have. Sounds obvious. Often ignored.

Human-in-the-loop. For high-risk actions, require a person to approve. Define what counts as high-risk for your context: financial transactions, customer communications, changes to critical records. When in doubt, add a human. You can always remove them later if the agent proves reliable.

Audit and log. Record what the agent did, when, and with what inputs. When something goes wrong, you need to understand what happened. Logs should be tamper-resistant and retained according to your policy. This is not optional for regulated industries.

Test before you ship. Run the agent through realistic scenarios, including edge cases and failure modes. What happens when the API is down? When the user asks something out of scope? The demo will not tell you. Only testing will.

Review and iterate. Governance is not a one-time exercise. As you add capabilities or change the environment, revisit the checklist. Keep it simple, keep it practical. And for goodness sake, do not turn it into a 50-page policy document that nobody reads.