How do I build my first AI agent?
Step-by-step approach
First, define exactly what your agent should do. A good first project is something like 'summarize a webpage and save it to a file' or 'answer questions from a PDF.' Keep the scope small.
Next, choose a framework. LangChain is popular for general-purpose agents; CrewAI is good for multi-agent collaboration; AutoGen from Microsoft is another option. You can also build from scratch with an LLM API like OpenAI's or Anthropic's.
Then, wire up the tools your agent needs. For a web summarizer, you'd need a tool to fetch web pages and a tool to write files. Most frameworks have pre-built tools for common tasks.
Finally, implement the agent loop: send the goal to the LLM, parse its response for an action, execute that action, feed the result back, and repeat. Add a stopping condition like 'task complete' or a max step count.
- Define a narrow, testable goal
- Pick a framework or use a direct LLM API
- Connect 1–3 tools (e.g., web search, file I/O)
- Implement the observe-think-act loop
- Test with simple cases and add error handling
Testing and iterating
Run your agent on a few example inputs and watch what it does. Log every step so you can see where it goes wrong. Common issues include the LLM misunderstanding the goal or calling tools incorrectly.
Start with a low-cost model like GPT-3.5 or Claude Haiku for testing, then switch to a more capable model if needed. Costs can add up quickly if your agent makes many LLM calls per task.
Once the basic loop works, you can add memory, better planning, or multiple agents. But don't over-engineer early—get a working prototype first.
Common mistakes
- Trying to build a general-purpose agent right away instead of starting with a specific, simple task.
- Skipping logging and error handling, which makes debugging nearly impossible.
- Ignoring cost and latency—each LLM call adds up, so design for efficiency from the start.
