An AI agent is an autonomous program that uses language models (such as OpenAI’s models or Anthropic’s Claude) to think, plan and make decisions without constant supervision. In this guide I show how to go from idea to a working agent, with architecture, tools, real costs and the mistakes I see most often.

What is an AI agent

Unlike a simple chatbot that only answers questions, an AI agent:

  • Plans several steps to solve a problem;
  • Acts on external systems such as APIs and databases;
  • Iterates and corrects course when something goes wrong;
  • Automates complete workflows.

Real use cases

Customer service: an agent that answers questions, looks up the customer database and opens tickets automatically.

Sales: lead qualification by email, meeting scheduling and automated follow-ups.

HR: application screening, interview feedback and onboarding.

Finance: expense categorization, account reconciliation and automated reports.

5 steps to build your AI agent

1. Define the problem the agent will solve

Don’t start big. Pick one specific workflow that eats up your team’s time. “Qualify leads that arrive by email” is a good scope. “Optimize everything” is not.

2. Choose the architecture

Option A: simple agent. For direct, one- or two-step tasks. Tools: OpenAI Assistants API or Claude API. Approximate investment: USD 500 to 1,500.
Option B: multi-agent. For complex, multi-step processes. Tools: LangChain, CrewAI, LlamaIndex. Approximate investment: USD 2,500 to 7,500.
Option C: custom with RAG. For proprietary company data and, when needed, fine-tuning. Approximate investment: USD 3,000 to 15,000 or more.

3. Define the agent’s tools

An agent is only as good as the tools you give it. Example for a customer service agent:

# Agent tools
GET  /clients/{id}   # fetch customer data
POST /tickets        # open a ticket
GET  /email/{id}     # read an email
POST /send-email     # send a reply
GET  /calendar       # check availability

4. Build and test

Start in a test environment with fictional data and cover three scenarios:

  • the happy path, with a legitimate customer;
  • incomplete data, to test error handling;
  • an unusual situation, to test the fallback.

5. Launch and monitor

In production, keep a close eye on:

  • task success and failure rates;
  • execution time;
  • cost per run;
  • errors and exceptions.

Recommended technologies

Models: the GPT family (fast and affordable) or Claude (strong at reasoning and long texts).

Frameworks: LangChain (Python), CrewAI (multi-agent) and LlamaIndex (RAG).

Orchestration: Zapier, Make or n8n to connect APIs with little code.

Hosting: AWS Lambda, Railway or Replit.

Real costs: a sales agent example

ComponentMonthly cost
Model API (about 500k tokens)~USD 20
Agent hosting~USD 100
Database (Supabase)~USD 25
Monitoring~USD 30
Total~USD 175

If the agent saves 10 hours of manual work per week (about USD 500 per week), the monthly cost pays for itself in under two weeks.

Common mistakes

  • Trying to do everything at once instead of starting small.
  • Launching without monitoring and not knowing whether it works.
  • No fallback for errors, so the agent breaks in production.
  • Not testing with real data and getting surprises later.
  • Ignoring API costs, which can grow quickly.
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