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The same method will not suit a startup, a professional firm, a retailer and a SaaS team.
View industriesClear scope, practical review points and access to the people doing the work.
An agent is worth building when a job takes several steps, needs information from more than one system and happens often enough to matter. We define the scope, the tools it may use and the point where it hands over to a person.
The word "agent" gets used loosely. We use it to mean something specific: a system with a goal, a limited set of tools it is allowed to use, and a clear rule for when to stop and ask a human. That definition matters, because the failure mode of an unbounded agent is an expensive mess.
We write down exactly what the agent is responsible for and, just as importantly, what it must never attempt. This document is the contract the build is measured against.
Each system the agent can touch is listed with the specific permissions it needs. Read access by default. Write access only where it has been explicitly justified.
Before building the agent we assemble real examples with known-good outcomes. Without this you cannot tell whether a change made the agent better or worse.
The agent is built against the evaluation set and run in shadow mode on live work. You see its decisions next to what your team actually did.
It goes live handling the cases it performs well on and escalating the rest. The boundary widens only when the numbers support it.
Agents are the most oversold category in AI right now, so it is worth being blunt about where they hold up.
They work well when the task is bounded, the information needed is available in systems you control, and a wrong answer is recoverable. Answering "where is my order and can I change the delivery address" is a good agent job: the data is there, the actions are reversible, and the volume justifies the build.
They work badly when the task requires judgement your policies do not encode, when a wrong action cannot be undone, or when the agent would need broad write access across systems to be useful. We will not build an agent with unrestricted access to your production database, and you should be wary of anyone who offers to.
The practical middle ground is an agent that does the gathering and drafting, then presents a recommended action for a person to approve with one click. That pattern captures most of the time saving with a fraction of the risk, and it is what we build most often.
Every project is scoped individually. These tiers give you a realistic sense of investment.
Whether an agent is the right tool for the job, and what it would cost
From $2,000 AUD
Get a quote →One bounded agent with evaluation, guardrails and admin review
From $12,000 AUD
Get a quote →A chatbot answers questions. An agent completes tasks. If a customer asks where their order is, a chatbot tells them how to check; an agent looks it up and, where it is permitted, actions the change they asked for. Agents need far more design work around permissions, logging and escalation, which is why they cost more to build.
Three ways. First, least-privilege access: the agent only gets the specific permissions its defined job requires. Second, an explicit allow-list of actions rather than open-ended tool use. Third, approval gates on anything irreversible or financial. Every step is logged so you can audit what happened.
It helps, but it is not a blocker. Part of the build is assembling a retrieval layer over whatever you have — policies, past tickets, product docs. Where the source material is genuinely contradictory, we surface that during scoping, because an agent will expose those contradictions faster than any audit.
With an evaluation set built before development starts: real cases with known-correct outcomes. Every change is measured against it. We also run the agent in shadow mode against live work so you can compare its decisions with your team’s before it handles anything on its own.
A feasibility review starts from $2,000 AUD and will tell you honestly whether an agent is worth building. A single bounded agent typically starts around $12,000 AUD including evaluation and admin tooling. Multi-agent platforms are quoted after scoping.
Model choice depends on the task, the accuracy bar and the cost per run. We commonly use Anthropic Claude and OpenAI models, and we design so the model can be swapped without rebuilding the surrounding system. You are not locked to one vendor.
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