Most software waits to be told. An agent is given a job, works out the steps, and carries them out, reading a document, checking it against your data, updating a system, escalating when it is unsure. The value is not the model; it is the job it takes off a person.
That also makes it a different kind of risk. An agent acts, so the questions worth asking are about boundaries: what it may touch, what it must escalate, and how you would know if it were quietly getting things wrong.
None of these are about the model. They are the conditions that decide whether an agent is safe to let near real work.
A defined task with a start, a finish and a definition of done. An agent given a vague remit produces vague work, and nobody can tell whether it went well.
The decisions a person makes doing this job, stated explicitly. Where the rules live only in someone's head, writing them down is the project, and it usually improves the process before any software exists.
What it may read, what it may change, and what it must never touch. Permissions that match the job, not the convenience of whoever set it up.
A queue for the cases it should not decide, with the context a person needs to decide quickly. An agent that never escalates is not careful, it is overconfident.
Every action logged in a form someone can audit. When a customer asks why something happened, the answer cannot be that the system decided.
Sampled and compared against what a good result looks like, the same way you would review a new starter. Quality drifts quietly otherwise, and the drift is only visible if you are looking.
Rules first. If the task follows steps you can write down, rule-based automation does it more cheaply, more predictably and with less to go wrong. Reaching for an agent to run a fixed process is paying for judgement you do not need. An agent earns its place where the work needs reading and deciding, an email that could be one of six requests, a document that has to be checked against your data, a case where the right next step depends on what was found. That is judgement, and rules handle it badly.
A person keeps the parts where being wrong is expensive or irreversible. The design we recommend is an agent doing the routine majority end to end, with a clear line where it stops and hands over, and the escalation rate watched as a health measure, not treated as a failure. Most real deployments are all three: rules for the predictable, an agent for the judgement, a person for the consequential. The value comes from putting each in the right place rather than from choosing one. Getting this wrong is common: Gartner predicts over 40% of agentic AI projects will be cancelled by the end of 2027 (Gartner, 2025).
It starts with a Rapid Insight Workshop at a fixed $9,000 + GST, which finds the judgement work that is ready to hand over. A good share of what people arrive wanting to automate is not ready, and finding that out for $9,000 is the cheap version.
Build and running costs are on our AI agents page.
One task, defined properly, with the decisions a person makes on it written down and agreed. This is where most of the value is, and where projects that skip it come unstuck later.
The agent does the work and a person checks it, on real cases, with the disagreements recorded. That comparison is the evaluation set, and it is the only honest measure of whether it is ready.
Once the agreement rate holds, the agent takes the routine cases end to end and the exceptions keep going to people. The escalation rate stays on a dashboard someone actually looks at.
A chatbot answers. An agent acts: it reads, decides, and changes something in a system. That distinction matters because acting carries consequences a conversation does not, so an agent needs boundaries, logging, and a defined escalation path before it goes anywhere near live work.
Anywhere the cost of a wrong action is higher than the cost of a person doing it. Irreversible decisions, anything with a legal or safety consequence, and any process nobody has written down yet. If the rules only exist in someone's head, the first job is writing them down, not automating them.
The same way you would judge a person doing the job: sample the output, compare against what a good result looks like, and watch the rate of escalations. An agent that never escalates is usually not being careful, it is being confident, which is a different thing.
Access to the information the job depends on, in a form it can read reliably, with permissions that match what the agent is allowed to do. Most agent projects that stall are stalled on data access and process definition, not on the model.
Rules follow a path you defined and cannot handle a case you did not anticipate. An agent works out the steps for the job it was given, which is what you want when the input varies and what you do not want when it does not. Use rules for the predictable majority and an agent for the judgement, most real processes need both.
Model usage is small at business volumes. The ongoing costs that matter are monitoring, the review time to keep quality honest, and the changes needed as your process moves. Budget for those from the start, an agent nobody reviews degrades quietly, and that failure is harder to spot than an outage.
Most first agents are live within 2 to 12 months as a fixed-price Amplify build, and a narrow one sits at the short end. Before that, a three-week Rapid Insight Workshop at $9,000 + GST settles which job the agent does and where it hands back to a person. If you only want to test whether an agent can do the job at all, an Ignite prototype takes 4 to 8 weeks.
Hire if you have a steady pipeline of agent work for years. A senior developer in Adelaide earns a median of about $130,000, roughly $180,000 a year once super, leave and overheads are added, and finding one can take months. One hire is one skill set, while an agent project also needs process analysis, architecture and design. Apex gives you that team from $15,000 a month on a 6-month minimum.
A Rapid Insight Workshop is two sessions with your key people that map how work really gets done and where time and money are lost. It costs a fixed $9,000 + GST, and about three weeks later you get a prioritised roadmap, a business case and a product specification you keep either way.
An AI agent is software that owns a job end to end, such as invoicing or compliance checks, and only hands you the exceptions. A first agent is usually proven in an Ignite build over four to eight weeks, then run and improved under an Apex partnership (from $15K a month).