How to Build an AI Agent for Your Business

An AI agent can help employees find information, review documents, answer recurring questions or complete parts of a workflow. But a useful agent starts with a clear business need—not with a tool chosen in advance.

The information it uses, the systems it can access and the actions it is allowed to take all affect its reliability. This guide outlines seven steps to developing an AI agent for your business.

What is an AI agent?

An AI agent is a system configured to carry out a defined task using instructions, information and, in some cases, tools that let it interact with other systems. It may answer a question using approved documents, guide someone through a procedure or perform a limited action within a workflow.

The term is used broadly. An assistant that only searches documents has a different level of autonomy from an agent that can update a record or send a message. The more an agent can do, the more important its permissions, controls and human review become.

What kinds of AI agents can a business create?

• Document search agent: Helps employees find information in approved policies, procedures, guides and reports.

• HR support agent: Answers common questions about workplace policies and directs employees to the appropriate HR contact when needed.

• Finance support agent: Helps authorized users locate reports and understand defined indicators, with human review for consequential decisions.

• Administrative agent: Drafts summaries, organizes requests or supports routine follow-ups.

• Operations agent: Helps teams find procedures and information relevant to day-to-day work.

• Internal support agent: Answers recurring questions and directs users to the right resource or person.

These examples describe possible uses, not a need to build six separate agents. Begin with one task and expand only when the first solution proves useful.

Seven steps to create an AI agent

Step 1: Choose a specific use case

Identify the problem before selecting a platform. Ask:

• What task takes too much time?

• Where do employees struggle to find reliable information?

• Which steps follow a repeatable process?

• What would a successful outcome look like?

A practical first use case might be helping employees locate an approved procedure. It has a defined audience, identifiable information sources and results that can be checked.

Set a baseline where possible—for example, the time currently spent answering a common question—so you can later assess whether the agent helps.

Step 2: Prepare the information it will use

Identify the documents, databases or systems the agent needs for its task. Check who owns each source, whether it is current and who should be allowed to access it.

Then:

1. Remove or identify outdated and duplicate documents.

2. Make the approved version easy to recognize.

3. Clarify terms that have different meanings across teams.

4. Identify confidential or sensitive information.

5. Decide how updates will reach the agent after launch.

Connecting more documents does not necessarily produce better answers. A smaller set of well-managed sources may be more useful than a large collection with conflicting instructions.

Step 3: Choose the right technology

Select a tool based on the agent’s task, required integrations, access controls, expected usage and available support.

• A custom GPT can help a team prototype instructions and document-based responses within ChatGPT. Available capabilities and sharing controls depend on the organization’s workspace settings. A custom GPT may be a useful starting point, but it should not automatically be treated as an agent that can act across internal systems.

• Microsoft Copilot Studio may suit organizations working within Microsoft’s ecosystem. It supports the configuration of agents with knowledge sources and tools, subject to the environment’s settings, permissions and licensing.

• An integrated or custom solution may be appropriate when the agent must work with several business systems or follow specific approval rules. This requires a closer review of data quality, integration options, maintenance and security.

A prototype can help validate the use case before you commit to a larger implementation.

Step 4: Define its role, permissions and limits

Write down what the agent can answer, which sources it may consult and which actions it may take. Define when it should say it does not know, ask for clarification or refer the user to a person.

For example, an HR agent might explain an approved leave policy but refer an employee’s individual eligibility question to HR. A finance agent might retrieve an authorized report without being allowed to approve a payment.

Pay particular attention to:

• access to confidential information;

• actions that change data or communicate externally;

• approval requirements;

• records of what the agent did;

• who is responsible for reviewing errors.

Step 5: Configure and build the agent

Once its scope is clear, configure the instructions, connect approved information sources and add only the tools required for the task. Define how answers should be presented and, where appropriate, how the agent should point users to its sources.

For most business projects, this step involves configuring an existing AI model and its connections, rather than training a new model. That distinction helps set realistic expectations about the work involved.

Step 6: Test it with realistic situations

Test the agent before giving it broad access. Include ordinary questions as well as cases where the correct response is to stop or ask for help:

• an outdated procedure;

• two documents that disagree;

• an unclear request;

• a question outside the agent’s scope;

• a user requesting information they should not access;

• an attempted action that requires approval.

Have people who know the process assess the answers and actions. Record recurring errors, correct their causes and test again. An agent should be able to acknowledge uncertainty rather than invent an answer.

Step 7: Launch with a pilot and keep improving

Start with a limited group of users and a clear way to report problems. Explain what the agent is for, which information it uses and when employees must verify an answer with a person.

During the pilot, review measures tied to the original need: time saved, answer usefulness, unresolved requests, errors and user feedback. Assign someone responsibility for maintaining the sources and updating the agent as procedures change.

Expand access when the results and controls support doing so.

Should you build an AI agent yourself?

A team can create a basic prototype with accessible tools, especially for a narrow document search or drafting task. Specialist support becomes more useful when the agent must connect to internal systems, handle sensitive information or take actions that affect important business processes.

Mallette can support your AI project by helping you define the use case, prepare information, select an approach, establish controls, test the solution and plan its adoption.

The objective is an agent your team can use with confidence for a task that matters—not simply an agent that can answer questions.