AI agents have moved from answering questions to taking action. That’s a genuine step change in what automation can return, and it changes what you need in place before you connect one to a system that matters.

In Australia, the National AI Plan confirmed the government will lean on existing laws and industry regulators rather than a standalone AI Act. That position could shift. Commonwealth agencies are already working to make AI requirements mandatory, and sector regulators can act under the laws they already have.

The safety measures you need for AI are the same either way. If businesses put them in place now, they protect themselves today and avoid having to rebuild their systems later.

CRM and finance are where the return is

CRM, accounting, ecommerce and operations are where agent automation pays for itself. Those are the systems where work is repetitive, volume is high, and a few hours saved per person per week shows up in the numbers. They’re also where the actions carry real commercial weight, which is one of the main reasons businesses don’t integrate these functions with AI. The risk worth planning for is when the ai agent does something you didn’t intend, using access nobody thought to limit.

That’s a solvable problem, and you solve it the same way you’d handle any other system that can change important data or spend money. The businesses getting real value from agents aren’t the most cautious ones. They’re the ones who sorted out access, approvals and record-keeping early on in the process Rolling agents out in phases, with the controls built in, is what makes those connections earn their keep without unnecessary delays.

Here’s what to put in place first.

Start with work you can undo

Give agents reversible, low-impact tasks first. You get the speed without expecting perfect outputs from day one.

The test is simple. If the agent gets something wrong, what does it have access to, and how quickly can you reverse it? If the answer is “a lot” or “not quickly”, it shouldn’t be the first thing you automate.

The best tasks to start with are the ones where the agent drafts the work, prepares it, or organises it up for someone to check. It still does the heavy lifting. A person still presses go. That pattern alone covers a surprising amount of what teams want automated.

Get sign-off on the handful of actions that matter

Approval steps aren’t there to slow everything down. Used well, they do the opposite. Once the risky actions need a person, everything else can run on its own.

At a minimum, require sign-off on payments, promises made to customers, data being exported, and changes to how a system is set up. Those four cover most of the decisions you’d want a human making anyway.

Give each agent its own login, and only the access it needs

An agent shouldn’t run on a shared admin account or on a login that can access everything. Give it its own, and open up only the parts it needs for that specific role. This does two things. It limits how much an agent can affect if it behaves unexpectedly, and it means every action traces back to one login, so you have a clear audit trail if you need it .

Keep a record of what the agent does

If you can’t answer “what did the agent do, and why”, you can’t make it better.

Keep a record of what it was asked, what it did, and what happened as a result. That’s the raw material for improving instructions, tightening the process and giving it more to do based on evidence rather than guesses. It also means you can trace any action back to a specific setup and a specific approval.

Test how it fails, not just how it works

Most teams check that an agent is accurate and sounds right, then put it live. The more useful test is what it does when the conditions aren’t simulated.

Give it messy instructions, missing information, unexpected errors and goals that test its flexibility of output. Watch how it fails, what it tries next, and whether your controls are working. The agents that have been tested in this manner are the ones you can safely hand more responsibility to.

Write down who owns what

Questions about how you’re using AI are moving from the tech team to the boardroom. When they arrive, you want processes and documentation rather than finger-pointing.

For every process with an agent in it, write down what protections the supplier has built in, who is accountable, how to pause the agent, and who owns improving it. It’s a short document, but it can help to get internal approval over the line.

Get the order right

None of this is a reason to avoid automation. Rolling out in stages, getting sign-off on risky workflows, limiting access, keeping records and testing how things fail are what let you connect agents to the systems that have a bottom-line impact, and moving forward as the technology gets more capable.

Start with the reversible work, prove the controls work, then open up the systems where the return is.