How To Create Ai Agents

Ai Tools
1

I’m trying to learn how to create AI agents for a project, but I got stuck figuring out the tools, workflows, and setup. I’ve watched a few tutorials, and they all seem to skip important steps, so now I’m confused about where to start and what actually works. I need help understanding the basics and avoiding mistakes before I waste more time.

2

Start small or you’ll waste a week wiring stuff no one needs.

Use this stack:
Python
OpenAI or Anthropic API
LangGraph or plain code for agent flow
Pydantic for structured outputs
FastAPI for your app layer
SQLite or Postgres for memory and logs
Docker if you need deployment

A simple agent has 4 parts.

  1. Model
  2. Tools
  3. Memory
  4. Loop

Workflow:
User sends task.
Model decides next step.
If needed, it calls a tool.
Tool returns data.
Model updates plan.
Repeat until done.

Do not start with “multi-agent.” Most tutorials push that too early. One agent with 2 to 4 tools beats a messy swarm.

Example tools:
Web search
SQL query
File reader
Email sender

Minimal setup:
Make a tool function.
Describe it in plain English.
Pass tool schema to model.
Execute tool call in code.
Send result back to model.

If you’re stuck, build one agent that reads a PDF and answers questions. Then add memory. Then add tool use. Thats the clean path.

Big tip. Log every prompt, tool call, result, and error. If you dont log, debugging sucks.

3

Tutorials usually skip the boring part, which is the actual product decision: what exact job should the agent do, and what counts as success?

That comes before framework choice, honestly. I slightly disagree with @techchizkid on one thing: I would not even think about “memory” early unless your use case truly needs it. A lot of beginners add memory, then spend 3 days debugging why the agent is confidently remembering nonsense lol.

What helped me was treating an agent like a tiny worker with a contract:

  • input it accepts
  • tools it can use
  • rules it must follow
  • output format
  • failure behavior

If you define those 5 things first, the setup gets way less confusing.

A practical build order I’d use:

  1. Hardcode one task
    Example: “summarize support tickets and tag urgency.”

  2. Make the model return strict JSON
    Not vibes, not paragraphs, actual fields.

  3. Add one tool only if the base version fails
    Don’t add search, DB, browser, calendar, and 12 shiny things on day one.

  4. Add evaluation before adding features
    Save 20 real test cases and score accuracy manually. Kinda boring, super important.

  5. Only then wrap it in an API or UI

Biggest beginner mistake imo: building an “autonomous agent” before building a reliable single-step assistant. If one step isn’t stable, looping it just makes the wrong answer happen faster.

Also, if your project is internal, sometimes a workflow engine plus LLM calls is better than a true agent. People overuse the word agent a bit tbh.

So yeah, pick one narrow task, define success, force structured output, test it on real examples, then expand. Thats the part most videos weirdly skip.