How to Build an AI Agent for Your Business

What an AI agent is and what it is not

A chatbot answers questions. An AI agent takes actions. It reads a request, decides which steps are needed, calls tools such as your CRM, ERP, ticketing system or calendar, checks the results, and either finishes the job or hands it to a person. Under the hood it is a language model plus a set of tool definitions, permissions, a memory of the current task and guardrails. Tepia treats each of those parts as a design decision, not a default.

Agents work best on jobs that are frequent, follow recognizable patterns but vary in detail, and today consume staff time moving information between systems. Examples include rescheduling service appointments when a technician calls out, matching inbound purchase orders to open sales orders, triaging support tickets with an order lookup and a drafted reply, and chasing vendors for missing documents. Jobs that happen twice a month, or that depend on judgment nobody can write down, make poor first candidates.

Before any build, Tepia asks for the numbers behind the job: how many cases arrive each week, how long each one takes, and how often a mistake slips through. Those three figures set the baseline the agent has to beat and give everyone a fair way to judge the pilot. If nobody can produce them, the first deliverable is measurement, not an agent.

Steps to build an AI agent for your business

1. Pick one bounded job. Define the trigger, the finished state and what the agent may never do. Measure how the job runs today: volume, time per case and error rate.
2. List the tools. Each action becomes a function with a name, inputs and outputs, such as a lookup of order status or a proposal of open appointment slots. Tepia wraps your APIs as these tools and, where it helps, exposes them through the Model Context Protocol so several agents can share them.
3. Set permissions. Start with read access and draft actions. Decide which writes need human approval, and enforce those limits in code rather than in the prompt.
4. Ground the agent in your knowledge. Policies, product data and past resolutions go into a retrieval augmented generation index on a vector database, so answers cite your documents instead of the model’s guesses.
5. Choose the model. Compare OpenAI and Anthropic models, or Azure OpenAI when data must stay inside your Azure tenant, on your own test cases for accuracy, tool use reliability and speed.
6. Build an evaluation set. Collect 100 or more real past cases with the correct outcome, and run every change against them before release.
7. Pilot in shadow mode. Let the agent propose actions while staff still do the work, compare outcomes, then grant autonomy step by step as accuracy holds.
8. Monitor in production. Log every tool call, input and output, track escalations and overrides, and review samples every week.

Choosing the right level of autonomy for the agent

Autonomy is a dial, not a switch. Tepia sets it per action, and most business agents start in the first two rows.

Moving an action up a row should be a decision backed by evaluation results and override rates, recorded with a date and an owner. Moving it back down should be just as easy when accuracy slips after a model update.

How Tepia approaches AI agent builds

Agent work runs through Tepia’s six phases, with evaluation built into each one.

1. Discovery: process mapping with the people who do the job today, a review of the systems and APIs the agent will touch, and a data readiness check. Deliverables are an Investigation Summary, an Interview Summary and User Stories such as an agent proposing three new slots after a cancellation.
2. Design: a design questionnaire, moodboards and a style guide, then wireframes for the approval queue, the escalation view and the action log your team will work from.
3. Development and Testing: an Alpha run against the evaluation set and a Beta in shadow mode on live traffic. Test plans cover functional tool calls, user acceptance by the team, and non functional checks such as prompt injection, API rate limits and response time.
4. Training: hands on sessions on approving, correcting and escalating agent actions, since every correction feeds the evaluation set.
5. Launch: staged autonomy action by action, a transition from the manual process, and support reps watching the first weeks.
6. Support: issue resolution, model updates retested against the evaluation set, and feature requests such as new tools.

A single job agent often lands at the short end of Tepia’s typical ranges: Discovery 1 to 3 months, development and QA 2 to 6 months, launch about a month. Read our process.

Where AI agent projects go wrong

• Starting too broad. An agent for all of customer service rarely ships. One that answers where is my order usually does.
• Permissions in the prompt. Telling a model not to issue refunds is not a control. Tepia enforces limits in the tool layer.
• No evaluation set. Without past cases and correct answers, every model or prompt change is a guess.
• Trusting untrusted input. Emails and documents can contain instructions aimed at the agent, so treat them as data and keep sensitive tools behind approval.
• No owner. Someone on your team must review logs and decide on autonomy changes.

Go deeper with custom AI agent development, AI agents connected to your CRM and ERP, LLM evaluation and testing and how to scope an AI project, or talk to Tepia about the job you want to hand off.

Frequently asked questions