How to Build an AI Agent From Scratch: 10 Steps

An AI agent is a language model that can decide which steps to take and use tools (search, code, files, APIs) to finish a task — not just answer one question.

1. Define the role and goal

What will it do, for whom, and what does "done" look like? Narrow beats broad: "summarise new supplier invoices and flag overdue ones" works; "run my business" doesn't.

2. Design structured input and output

Define what the agent receives and returns as a schema, like an API. Use Pydantic AI or JSON Schema so outputs are always valid.

3. Write the system prompt

Give it a role, rules, and examples of good output. Connect tools in a standard way with MCP.

4. Add reasoning and tools

Let it think step by step and call tools: web search, a code interpreter, document retrieval. Frameworks: LangChain or the OpenAI Agents SDK.

5. Multi-agent logic (only if needed)

Split complex jobs into roles — planner, researcher, writer, reviewer — each with its own input and output. Use CrewAI or LangGraph. Start with one agent; add more only when it struggles.

6. Memory and long-term context (RAG)

Store documents and past conversations in a vector database like Chroma so the agent can look things up instead of guessing.

7. Voice and vision (optional)

Add speech with ElevenLabs or Whisper; add image understanding with a vision-capable model.

8. Deliver usable output

Return Markdown, JSON or a PDF that other software or a person can use directly.

9. Wrap it in a UI

Gradio or Streamlit for a quick web app; FastAPI if other apps will call it.

10. Evaluate and monitor

Keep a set of test tasks with known answers and re-run them after every change. Log each tool call so you can see where it goes wrong.

Running it free

Ollama runs open models on your own computer, and Google Colab gives you free cloud notebooks to experiment.