Guide
How to build AI agents for business: start with the right work
To build the right AI agents for your business, start from high-value, repetitive tasks, give each agent a defined scope, tools and decision limits, and keep people in the loop for high-risk decisions.
What is an AI agent?
An AI agent is software that uses a large language model to reason, plan and carry out tasks through tools, with limited supervision and within limits that people set.
The difference from a chatbot is action. A chatbot answers questions; an AI agent also does things, such as reading a record, updating a ticket or drafting a reply, through tools connected to business systems. Without tools, an agent is only a chatbot.
An agent works with limited supervision, not with none. People decide what each agent is for, which systems it may use and which decisions it must hand back. When many tasks are shared between people and agents in this way, with people setting goals and overseeing the work, the organization has what is often called an agentic workforce: see what is an agentic workforce. The terms in this guide are defined in the agentic AI glossary.
Which work should your first AI agents do?
Your first AI agents should take on high-value, repetitive tasks, not whatever the newest model can do. Choose work that happens often, follows clear rules and is cheap to correct when something goes wrong.
A good first task is frequent, stable and well understood. It runs many times a week in much the same way, its inputs arrive in a predictable form, and the people who do it today can explain its rules. Work that depends on judgment, negotiation or empathy is a poor first candidate, even when it is expensive. Start narrow: one task, one team and one set of systems. Check each candidate task against the criteria below.
| Criterion | Why it matters | How to check it |
|---|---|---|
| Volume | Frequent work repays the effort of building and governing an agent. | Count how often the task runs each week, from system records or recorded work rather than estimates. |
| Repetition | An agent copes best when a task follows the same few paths. | Compare several runs of the task and note how many different ways people complete it. |
| Rules versus judgment | Rules can be written down and tested; judgment is much harder to hand over. | Ask whether two experienced people would make the same decision on the same case. |
| Data access | An agent can only act on data it is allowed to read. | List the data each step needs, who owns it and who may grant access to it. |
| Systems touched | Every system an agent uses needs a safe, permitted connection. | Name each application the task uses and check whether it offers a secure API. |
| Cost of an error | Cheap mistakes allow learning; costly ones need a person's approval. | Describe the worst realistic error, who would notice it and how it would be put right. |
| People affected | Customers, colleagues and suppliers may all feel the change. | Identify who receives the output, and whose work changes when the agent takes over. |
Why start from evidence instead of interviews?
Start from evidence because interviews and workshops describe the official process and forget the workarounds. Map the work from records of what actually happens before you design an agent.
Interviews still matter, because they explain why work is done a certain way. But memory is selective. Exceptions, rework and the small steps between applications rarely come up in a workshop, and they are exactly where agents fail.
Two kinds of evidence help, and Optimus Hive offers both. On Windows desktops, Opus (task mining) captures the applications and websites in use, their window titles and the time spent in each. Opus also costs every recorded task case from its duration and the department's hourly or daily rate, and its task process map shows each task's variants with their average case cost, so expensive variants stand out. Across business systems, Favus (process mining) builds a process map from an event log of case IDs, activities and timestamps.
To see how the two kinds of evidence fit together, read how Opus, Favus and Via fit together. For the steps that follow, see how to identify automation opportunities and how to prepare work for AI agents.
How do you define an agent's scope, tools and limits?
Define an agent's scope, tools, knowledge sources and decision limits in writing before anyone builds it. A short definition sheet keeps business owners, IT and risk teams working from the same description.
Write the sheet in business language and keep it short enough to review in one meeting. The decision limits matter most: they say what the agent may decide alone, what it must propose for a person to approve, and what it must never do.
| Field | The question it answers |
|---|---|
| Goal | What business result is this agent for, and how will we know it is working? |
| Tasks in scope | Which tasks, steps and kinds of case does the agent handle? |
| Tasks out of scope | What must the agent refuse or pass on, even if it could do it? |
| Tools and systems | Which applications and APIs may the agent use, and with which permissions? |
| Knowledge sources | Which documents, records and policies may the agent read to do its work? |
| Decision limits | What may the agent decide alone, what needs a person's approval, and what is forbidden? |
| Escalation path | Who receives a case the agent cannot finish, and through which channel? |
| Business owner | Which named person is accountable for the agent's results and for changes to it? |
How should an agent connect to your systems?
An agent should connect to business systems through secure APIs, with its own credentials and only the permissions its tasks need. That keeps its access visible, limited and easy to withdraw.
In practice, a few rules cover most of the risk:
- Give each agent its own identity. Never let it use a person's login or a shared account.
- Grant least privilege. Allow reading where reading is enough, and writing only to the records its tasks change.
- Prefer documented APIs to screen automation. An API call can be authorized, limited and logged.
- Log every call. Record the inputs and the result of each action, so it can be traced later.
- Keep secrets out of prompts. Store passwords and keys in the systems built for them, never in the documents an agent reads.
- Review access when the scope changes. A new task or a new system is a reason to check the agent's permissions again.
When must a person stay in the loop?
A person must stay in the loop wherever an agent's decision is high-risk: when it commits money, changes important records, affects a customer or cannot easily be undone. Roll agents out in stages, with a check at each stage.
A staged roll-out turns an unmeasured pilot into a controlled one. In a common sequence, the agent first drafts work for a person to finish; next it acts on low-risk cases while people review a sample; its limits widen only when the measured results justify it. Each stage has an owner, a measure and a point where someone decides whether to continue. The guide to safe AI agents explains how to match oversight to risk.
How do you plan the move from people to agents?
Plan the move from people to agents as a governed program: decide which tasks change performer, in which order, at what cost, and who approves each change.
Two habits help. First, agree on the approved way of working before any agent copies it; in Opus, analysts can mark the best recorded run of a task as the approved method. Second, run the change as a program of initiatives and change requests, so that each step has an owner, a plan and a decision. Via (transformation planning), the third product of Optimus Hive, plans the move as such a governed program, with initiatives, change requests, a Kanban board and a Gantt timeline. Read about Via agentic workforce transformation and agentic workforce planning.
To set and track the value of each change, read the AI value realization guide. For help with the plan itself, see AI transformation consulting from AI Transitioning.
Questions about building AI agents
These are short answers to questions leaders ask before they build their first AI agents.
How many AI agents should we start with?
Start with as few agents as you can learn from, often a single agent for a single well-understood task. A narrow first agent lets you test the definition sheet, the system access and the human review before you scale. Add agents when the first one meets its measures and its owner trusts its work.
Do we need task mining or process mining before building AI agents?
You need evidence of how the work runs today, and task mining and process mining are two ways to get it. Task mining shows the steps people take inside a task at their desktops; process mining follows each case across the business systems that record it. Which you need depends on where the work happens: compare them in task mining vs process mining.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions, while an AI agent carries out tasks through tools. A chatbot holds a conversation and returns information; an AI agent plans steps and acts on business systems, such as updating a record or drafting a reply. The tools, permissions and limits you give an agent decide what it can do.
Plan your first AI agents with AI Transitioning
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