How to build an AI agent for your business: eight steps from process to launch

Connecting a language model to a couple of tools is now a quick job. Getting that agent to perform on your process, with your data and your edge cases, is a different discipline. This guide is for founders and operators who have to decide how to do it, whether or not they write code. If you want the basics first, start with what AI agents are.

Where do you start when building an AI agent?

You start with the task and the success criteria, not the technology. Anthropic's documentation opens on exactly that point: a successful LLM application begins by defining success criteria and then designing evaluations to measure against them. Write one page covering who uses the agent, what it receives, what it must produce, which actions it may take alone and how you will know it is working. If that page stays blank, the problem is not ready for an agent.

An example: inbound support for a B2B SaaS

A small SaaS company gets a steady stream of support emails: password resets, invoice copies, feature questions, the occasional bug report. The agent classifies each message, answers account and billing questions using the help centre and the billing API, attaches invoice PDFs on request and turns bug reports into tickets with reproduction steps for the engineers. Success means time to first response and the share of conversations closed without a human, with a weekly check on customer satisfaction.

Which tools do you need: no-code, a framework or custom code?

It depends on who will maintain the agent, how many integrations it needs and how much traffic it will see. There are three main routes, and they combine well: plenty of agents start on a low-code platform and move to code once they grow.

Anthropic's engineering team recommends starting with the model APIs directly, since many patterns take only a few lines, and understanding the underlying code if you do adopt a framework. For connecting tools, the Model Context Protocol is an open standard supported by Claude, ChatGPT and many developer tools: rather than writing every integration yourself, you plug in existing MCP servers, after vetting who maintains them and what they can access.

How do you write instructions and tools that work?

Write them like a runbook for a new, very literal colleague: goal, steps, exceptions, limits. A few rules that hold up in practice:

• Reuse what you already have. Standard operating procedures, support macros and policy documents are, per OpenAI's agent guide, the best starting point for agent routines.
• One clear action per step. “Ask for the order number” beats “gather the relevant information”.
• Spell out the edge cases. Missing data, off-topic requests, angry customers: if you do not say what to do, the agent improvises.
• Document tools as you would for a colleague. Clear name, purpose, parameters, return values. Anthropic calls this the agent-computer interface and advises documenting and testing it carefully.
• Keep data and commands apart. Text inside an email or web page must never be able to rewrite the agent's rules.

How do you test an AI agent before it goes live?

You test it with evals: a set of real cases you rerun after every change. Because models can answer the same input differently, conventional tests are not enough. OpenAI's evaluation guide sets out five moves: define the objective, collect a dataset, choose metrics, run and compare, and evaluate continuously on every change. It also warns that starting with a multi-agent design adds complexity your evals should justify first.

What goes into the eval set

• Typical cases drawn from recent real requests.
• Hard cases: missing data, ambiguous wording, other languages.
• Cases the agent must refuse: off-scope requests and manipulation attempts.
• For each case, the answer or action you consider correct.

Grading can be largely automated: exact match for extracted fields, a second model acting as judge for free text, and human review on a sample. Anthropic's guidance favours many automatically graded cases over a handful graded by hand. A sceptical eval set is healthy: in Stack Overflow's 2025 developer survey, even the people who build with agents say they worry about their accuracy.

What guardrails does a production agent need?

It needs limits on what it can do, how long it can keep trying and when it must stop.

• Retry limits. If the agent cannot work out the request after a few attempts, it hands over. OpenAI names exceeded failure thresholds as one of the two typical triggers for human intervention.
• Least privilege. Read access where reading is enough; write access only to the fields that matter.
• Action log. Every tool call stored with its inputs and outputs, so you can debug mistakes and explain what happened.
• Spend caps. A ceiling on steps and cost per request, with an alert as it approaches.

How do you launch and maintain an agent?

Roll it out gradually and monitor it like any other production system, plus a watch on answer quality. Early on, the agent proposes and a person approves; each week you review the success metrics from step one, the failures and the cost per request. When a provider updates or retires a model, your evals tell you straight away whether the switch breaks anything.

Build and running costs deserve their own analysis, with third-party ranges and what drives them: see AI agent development cost. If the agent must answer from your company's documents, retrieval-augmented generation is usually the right pattern, covered in our LLM application development guide.

Which rules should you check before launch?

If the agent serves people in the EU, check three things before launch: transparency towards the people it talks to, a lawful basis and privacy notices for any personal data, and a data processing agreement with the model provider. From 2 August 2026 the AI Act requires people to know when they are interacting with an AI system unless it is obvious, and the GDPR applies to whatever personal data flows through the agent. Our guides to the EU AI Act for startups and GDPR and LLM apps go deeper; take professional advice for your specific case.