Most businesses don’t need a 50-page AI strategy to get started with AI agents. They need one well-chosen process, a clear owner and a way to measure whether it worked.
That matters, because a lot of agent projects are going nowhere. In June 2025, Gartner predicted that over 40% of agentic AI projects will be cancelled by the end of 2027. The reasons were rising costs, unclear business value and weak risk controls. None of those are technology problems. They are planning problems, and you can avoid them.
This guide walks through how to start, step by step.
What is an AI agent?
A chatbot answers questions. An AI agent gets a job done.
You give an agent a goal. It works out the steps, uses your systems to carry them out, checks the result and either finishes or hands back to a person. For example, an agent might read an inbound enquiry, look up the customer in your CRM, draft a reply, book a call and log the activity.
Be careful with vendor claims. Gartner also found that many products sold as “agents” are rebranded chatbots or automation tools. It estimates only about 130 of the thousands of agentic AI vendors offer the real thing. Ask any vendor to show you the agent completing a task end to end, in your systems.
Step 1: Pick one process, not a platform
Start with the work, not the tool. The best first agent project has these traits:
- High volume. It happens dozens or hundreds of times a week.
- Repetitive. Most cases follow the same pattern.
- Clear rules. A new staff member could learn it from a written guide.
- Digital inputs. The information arrives as emails, forms, documents or system records.
- Low cost of error. A mistake is annoying, not dangerous, and a person can catch it.
Good first candidates include enquiry triage, support ticket routing, invoice matching, meeting notes to CRM updates and new client onboarding paperwork.
Avoid starting with anything that makes final decisions about people, money or safety. Those can come later, with stronger controls.
Step 2: Write down how the work is done today
An agent can only follow a process you can describe. Map the current steps, the systems involved and the decisions made along the way. If you have standard operating procedures, start there.
Then measure your baseline:
- How many times does this task happen each week?
- How long does each one take?
- How often does it go wrong?
- What does it cost in staff time?
Without a baseline, you can’t prove the agent worked. That is how projects end up in the “unclear business value” pile.
This step often exposes a broken process. Fix it first. Automating a bad process just produces bad results faster.
Step 3: Get your data and systems ready
Agents act through your systems, so they need access. List every system the agent must read from or write to, such as your CRM, inbox, finance software or document storage.
Check three things for each one:
- Access. Is there an API or integration the agent can use?
- Permissions. Give the agent its own account with the least access it needs. Never share an admin login.
- Data quality. Messy, duplicated or outdated records will produce messy results.
If the agent handles personal information, your privacy obligations still apply. Know where the data goes, which AI model processes it and whether the provider stores it.
Step 4: Set guardrails and keep a person in the loop
The Australian Government’s National AI Centre released its Guidance for AI Adoption in October 2025. It replaced the earlier Voluntary AI Safety Standard and sets out six essential practices:
- Decide who is accountable
- Understand impacts
- Measure and manage risks
- Share essential information
- Test and monitor
- Maintain human control
In practice, for a first agent, that means:
- Name one person who owns the agent and its results.
- Require human approval before anything goes to a customer or moves money.
- Log every action the agent takes, so you can review what happened.
- Tell customers when they are dealing with AI.
- Record the agent in an AI register. The National AI Centre provides a free template.
Start with the agent drafting and a person approving. Give it more autonomy only once the results prove it has earned it.
Step 5: Run a pilot, measure, then decide
Run the agent on real work for 30 to 60 days, with your guardrails in place. Track the same numbers you measured in step 2.
At the end, make a clear call:
- Scale it if it saves real time at an acceptable error rate.
- Adjust it if it is close but needs better instructions, data or access.
- Stop it if the numbers aren’t there. A stopped pilot that cost a few weeks is a good outcome. A two-year project that quietly fails is not.
Buy or build?
Check the tools you already pay for first. CRMs, help desks and office suites are adding agent features, and these are often the fastest and cheapest place to start.
A custom agent makes sense when:
- The process runs across several systems that don’t talk to each other.
- The process is part of what makes your business different.
- Off-the-shelf tools can’t meet your privacy, security or data residency needs.
Custom agents are usually built on models from providers such as OpenAI, Google or Anthropic, connected to your systems through APIs.
Five mistakes to avoid
- Starting with the technology instead of the business problem.
- Automating a process nobody has written down.
- No named owner.
- No baseline, so no way to prove value.
- Full autonomy on day one.
Frequently asked questions
How long does it take to deploy a first AI agent?
A focused pilot on one process usually takes a few weeks to set up, plus 30 to 60 days of running it. Timelines grow with the number of systems involved.
How much does an AI agent cost?
It depends on the number of systems, how complex the process is and whether you buy or build. Ongoing costs include model usage, hosting and monitoring. Always compare the total against the staff time you measured at the start. We break this down further in how much it costs to build and deploy an AI agent in Australia.
Do we need data scientists?
Not for most first projects. You need someone who knows the process well, someone who can manage the integrations and a clear owner.
Are AI agents safe to use with customer data?
They can be, with the right controls. Use least-privilege access, keep a person approving external actions, log everything and check how your AI provider stores and uses data.
Where to from here
Whitetower helps Australian businesses find the right first AI use case, build it and govern it properly. Read more about our AI Development services and Forward Deployment Engineering, or talk to our team.
