AI Agents That Finish the Job, Inside Limits You Set

Workflow automation follows a path you define in advance: when X happens, do Y, then Z. An agent is given a goal and decides the path at run time, choosing which tools to call based on what it finds. Automation is cheaper, faster and more predictable, so it is the better choice whenever the steps are known. Agents earn their cost on variable work where the right next step depends on reading and judging the situation. Many good systems combine both, with an agent handling the judgement step inside an otherwise fixed workflow.

By design rather than by prompt. The agent can only call tools we have built for it, each with scoped credentials, validated parameters and rate limits. Irreversible or high value actions require human approval by default. Every run has step, time and spend ceilings. Content the agent reads is treated as untrusted data, which limits prompt injection. And every action is traced, so anything unexpected can be investigated and the rules tightened.

Usually not at first. A single agent with a well designed set of tools handles most business tasks and is easier to test and debug. Multiple agents help when a job splits into parts that need different tools, context or permissions, for example a researcher that can only read and a writer that can only draft. We add agents when the evaluation suite shows a measurable gain, not because the architecture looks impressive.

We choose per task after testing candidates on your scenarios. Hosted models from Anthropic and OpenAI are common choices for agent work because tool use and multistep reasoning are their strengths, and open weight models can suit narrower tasks or strict sovereignty needs. Model availability in Australian cloud regions changes often, so we confirm the current position at the time of the engagement and document where every call is processed.

Overseer is our internal delivery pipeline. It runs multiple AI coding agents in parallel, and every change they produce passes type checks, visual tests and security scans, then a senior engineer's review, before deployment. It is not a product we sell. It matters because we operate agents under quality gates every working day, so the failure modes we design against in your system are ones we have seen first hand.

A focused agent for one task typically takes 8 to 12 weeks from task selection to supervised production use, including the scenario suite and a shadow run. Agents that touch many systems, need complex approval rules or run across teams take longer. Every build starts with a Discovery Audit, a fixed fee credited in full against the build. After two strategy sessions you receive a fixed price Schedule of Work with three options: The Minimum, The Recommended and The Complete.

Running cost is mostly model usage, which scales with the number of steps per run and the amount of context each step reads, plus hosting and any third party APIs. Agent runs usually cost more per task than a single model call because they involve several calls. We measure cost per completed run during the shadow period and set per run spend limits, so you know the unit cost before you widen autonomy.

Every build is priced on its own scope. It starts with a Discovery Audit (a fixed fee, credited in full against the build). After the audit you get a fixed price Schedule of Work with three options: The Minimum, The Recommended and The Complete. The option you choose is paid in three stages against work you have accepted. We never publish day rates.

AI agents for business are best at variable, multistep work that currently needs a person to read, look things up and decide, such as reconciling supplier statements, triaging inbound requests or preparing a first draft of a tender response. They are a poor fit where every step is already fixed or where there is no clear definition of done. The useful question is which of your recurring tasks has enough volume and a checkable end state to justify an agent.

We choose per project, most often the Claude Agent SDK, the OpenAI Agents SDK or LangGraph for the agent loop, the Model Context Protocol for reusable tools, Temporal for durable execution, and OpenTelemetry or Langfuse for tracing. The framework matters less than the tool design, permissions and evaluation suite around it, so we keep business logic out of framework specific code where we can, which makes it easier to move later.

No. Robotic process automation (RPA) replays a scripted sequence of clicks and keystrokes through existing screens, so it breaks when a screen or input changes and cannot handle cases outside the script. An AI agent decides its next step from what it reads, and in our builds it acts through APIs and typed tools rather than a user interface. RPA can still be the better choice for a stable, high volume process on a system with no API.

Published Australian figures put a focused AI agent at about $5,000 to $50,000 (AUD) to build, and agents that span several systems with approvals and audit logs from around $40,000 to several hundred thousand dollars. Running costs recur monthly: model usage for a moderate workload is often in the hundreds of dollars a month, plus hosting, monitoring and maintenance. Those are market ranges, not our quote; your price is fixed in a Schedule of Work after the Discovery Audit.

Cheaper to start, usually, but not always cheaper to run. Platform agents such as Microsoft Copilot Studio, Salesforce Agentforce or n8n charge per credit, action or execution, so the bill grows with every run. They suit tasks that live inside a platform you already license. A custom agent costs more up front, but its unit cost is mostly model usage you control, and you own the code. We compare both on your volumes before recommending either.

The build is a fixed price, and running costs are based on usage rather than seats. Your Schedule of Work sets out three options, The Minimum, The Recommended and The Complete, paid in three stages against work you have accepted. Once live, the costs are model usage and hosting in your own cloud account, which we measure as cost per completed run during the shadow period. A Care Plan can cover operating and extending the agent.