01
Job definition
The task, the inputs it receives, what a finished result looks like and what the agent must never do.
AI Agents / focused landing page
AI agentsAI Agents for Business with a defined job, the right context and a human hand-off.
AI agents for business that answer enquiries, qualify leads, draft follow-ups and prepare reports, with clear limits and escalation to your team.
An AI agent should own one clear job, use approved information and pass anything uncertain to a person.
AISB growth brief
An AI agent should own one clear job, use approved information and pass anything uncertain to a person.
FocusAI Agents
SystemHuman strategy × AI power
NextDefine the job
Make the next move useful
What an AI agent actually does in a business
An AI agent is software that can read a request, decide which steps to take, use your systems or documents to complete them and report back. That makes agents useful for work that is too varied for simple rules, such as answering product questions from your own documentation, qualifying an inbound lead or preparing a first draft of a proposal. It also makes them risky if the job, the permitted actions and the escalation path are vague. We define all three before building anything.
One agent, one job: a clear task, a clear finish line and a clear owner.
Agents only use information and systems you have approved.
Anything uncertain, sensitive or high-value is handed to a person.
Agent design
What goes into a business AI agent
A working agent is mostly design and testing. The model is only one part of it. These are the pieces we specify and test with you.
02
Knowledge and access
The documents, FAQs, CRM fields or tools the agent may use, with permissions limited to what the job needs.
03
Escalation rules
When the agent stops and hands over to a person, and what context it passes along so nobody starts from scratch.
04
Testing and monitoring
Real examples run before launch, then ongoing review of conversation logs, errors and hand-off rates.
Is it a fit?
Who AI agents suit
Businesses receiving the same customer questions repeatedly through the website, email or chat.
Sales teams that lose leads because nobody can respond or qualify them outside office hours.
Teams with good internal documentation that staff still spend time searching through.
Owners who want first drafts of routine emails, summaries or reports prepared before a person reviews them.
When it is probably not the right fit
If the work needs licensed professional judgement, involves irreversible financial decisions, or your source information is out of date, an agent is the wrong tool. A simpler automation or better documentation will serve you better.
From job to launch
How we build and launch an AI agent
Each agent starts as a narrow pilot with real examples from your business. We would rather launch an agent that does one job reliably than one that attempts everything and needs constant correction.
Our recommendations are grounded in recognised industry guidance, including OECD AI Principles.
1
Define the job
Agree the task, success criteria, permitted actions and escalation triggers with the person who will own the agent.
2
Prepare the knowledge
Gather and clean the documents and data the agent relies on, and restrict access to what the job needs.
3
Build and test
Run the agent against real past enquiries or tasks and fix wrong answers before anyone outside the team sees it.
4
Launch with oversight
Go live with logs, review routines and a clear process for improving answers over time.
Types of agent
Common AI agents for small and mid-sized businesses
Most useful agents fall into a few categories. Each has its own page explaining scope and limits.
Cost
What affects the cost of an AI agent
The main cost drivers are how many systems the agent connects to, how much source material needs preparing, how many types of request it must handle, and how much testing the risk level demands. Model usage is an ongoing running cost that grows with volume. We scope a single job first so costs are visible before you commit to more.
FAQ
Questions buyers ask before starting
Is an AI agent the same as a chatbot?
Not quite. A traditional chatbot follows scripted paths. An AI agent interprets the request and can take steps in other systems, such as checking availability or updating a CRM record. Many businesses need a well-built chatbot rather than an agent, and we will say so.
Can an AI agent make mistakes?
Yes. Language models can give wrong or invented answers. That is why agents are limited to approved information, tested on real examples before launch, and designed to hand uncertain cases to a person.
What happens to our customer data?
We design agents to access only the data their job requires and document where information is processed. Privacy obligations under the Australian Privacy Act or New Zealand Privacy Act 2020 are part of the design, not an afterthought.
Can the agent work with our existing CRM and website?
In most cases, yes, provided the systems have an API or supported integration. We confirm this before the build is scoped.
Make the next move count
Have a job you think an AI agent could do?
Start with the Growth Systems Audit to see where response times, follow-up and repetitive handling are costing you, then decide whether an agent is the right fix.
