How to build an AI agent: a practical guide

The basics

What is an AI agent?

An AI agent is software that uses a large language model to understand a request, decide what to do and take action with tools, such as looking up a record, booking a slot or sending a message. It then reports back or hands over to a person. The difference from a chatbot is the action: a chatbot talks, an agent gets something done.

AI agent, chatbot or automation?

If a task never changes, a simple automation such as Power Automate is cheaper and more predictable. Use an agent when requests arrive in messy, human language and still need action.

The building blocks

The parts of an AI agent

• A model, the large language model that reads, reasons and writes.
• Instructions, plain-language rules for its job, its tone and its limits.
• Knowledge, your documents and data, usually connected through retrieval augmented generation (RAG).
• Tools, the actions it may take, such as calling an API, updating a CRM record or sending a WhatsApp message.
• Memory, what it keeps track of within a conversation or a task.
• Guardrails, the limits, approvals and handover rules that keep it safe.
• A channel, where people reach it: Microsoft Teams, your website, WhatsApp or the phone.

Step by step

How to build an AI agent in 8 steps

Step 1: Pick one job, not ten

The agents that make it to production solve one narrow problem for one group of people. Write the job as a single sentence that names the input, the output and how you will know it worked.

Example: when a client asks on WhatsApp for a copy of an invoice, find it in the accounting system and send it within a minute, or hand over to the accounts team if the client is not recognised.

Good first jobs are frequent, repetitive and low risk: answering the same questions, booking appointments, chasing overdue invoices or routing requests.

Step 2: Gather and clean the knowledge it needs

An agent is only as good as the information behind it. Collect the documents, FAQs, price lists, policies and past conversations that cover the job. Remove old versions and duplicates, and note who owns each source so it stays current.

If your files live in scattered folders, organising them first pays off twice: the agent answers better, and your staff find things faster. A tidy SharePoint intranet is a strong foundation.

Step 3: Choose how you will build it

There are two broad routes:

• Low-code platforms such as Microsoft Copilot Studio let you build an agent in a visual designer, connect it to Microsoft 365 and publish it to Teams or a website. For most businesses already on Microsoft 365, this is the fastest route.
• Code-first platforms and frameworks, such as Microsoft Foundry on Azure, give developers full control over models, tools and hosting. Use them when the agent needs complex logic, custom integrations or high volumes.

Pick one route, ship the narrow job, and learn what you actually need before you add complexity.

Step 4: Ground it in your own information

To stop an agent from guessing, connect it to your knowledge with retrieval augmented generation (RAG). Your documents are split into passages and turned into embeddings, numerical fingerprints of meaning. When a question comes in, the agent finds the most relevant passages and answers from them, ideally with a reference to the source.

Grounding is the biggest single factor in whether people trust the answers. We go deeper on our custom company AI page.

Step 5: Give it tools, with the least access it needs

Tools turn an assistant into an agent. Each tool is one action with a clear name and description, such as look up an invoice by client number. The model reads those descriptions to decide when to use each tool, so write them as carefully as you would brief a new staff member.

Connect tools through your systems' APIs and connectors, or through an open standard such as the Model Context Protocol (MCP), which Microsoft Copilot Studio and many other platforms support. Give each tool only the access it needs: an agent that sends invoices does not need permission to delete them. Our guide to AI agent protocols explains MCP and A2A in plain language.

Step 6: Set guardrails and a human handover

Decide up front what the agent may do on its own, what needs a person's approval and what it must never do. For example, it may draft a payment reminder, but a person approves the first one sent to each client.

• Tell it to say when it does not know, instead of guessing
• Hand over to a person on request, on complaints and on anything outside its job
• Log every action, so you can see what it did and why
• Collect only the personal information the job needs, in line with POPIA

Step 7: Test it with real questions

Before launch, build a test set from real messages your business receives, including the awkward ones: rude messages, half-finished questions, requests outside the job and attempts to trick it. Check every answer for accuracy, safety and tone, fix the instructions or the knowledge, and test again.

Keep this test set. Every time you change the agent, run it again, so a fix in one place does not break something else.

Step 8: Launch small, then improve

Launch on one channel with a small group first, such as one team in Microsoft Teams or after-hours WhatsApp messages. Review the conversations every week, look for wrong answers and missed handovers, and improve. Then widen it to more people and more channels.

Name an owner inside your business. An agent needs someone to keep its knowledge current, the same way a new staff member needs a manager.

Avoid these

Common mistakes when building an AI agent

• Starting with a job that is too broad, such as handle all customer service
• Connecting it to messy, outdated documents
• No clear handover, so frustrated customers get stuck
• Giving tools more access than the job needs
• Skipping testing because the demo looked good
• Launching with no owner to review and improve it

Your options

Build it yourself, or work with AI agent builders?

If the job is simple and internal, such as an assistant that answers staff questions from your policies, a low-code tool and this guide may be all you need.

Bring in AI agent builders when the agent must connect to several systems, answer phone calls or WhatsApp, handle personal information or serve your clients directly. That is where integration, security and testing take most of the effort.

OMNATIX builds AI agents for South African businesses on Microsoft Copilot Studio and Azure AI. We start with a data-gathering session to understand your work, then implement your agent, and you use it free for 7 days. We are a Level 1 B-BBEE contributor.