How To Build Ai Agents

Ai Tools
1

I’m trying to learn how to build AI agents for a small project, but I got stuck choosing the right tools, frameworks, and setup. I’ve read a few guides on AI agent development, but most of them skip steps or assume too much. I need help understanding the basics, best practices, and what I should focus on first.

2

Start small or you’ll waste a week wiring tools you do not need.

For a small agent project, use this stack:
Python, FastAPI, OpenAI or Anthropic API, SQLite or Postgres, and one framework max. I’d pick LangGraph if you need multi-step flows. If you do not, skip frameworks and write plain Python first.

A simple setup:

  1. Define one job. Example, read support email, classify it, draft reply.
  2. Write the loop in code. Input, prompt, model call, output.
  3. Add tools only when the model needs outside data. Search, database read, API call.
  4. Log everything. Prompt, response, tool call, latency, cost.
  5. Add evals. Test 20 to 50 real examples. Score accuracy, tool errors, token cost.

Rule of thumb:
If your flow has under 5 steps, don’t use an agent framework yet.
If you need branching, retries, memory, human approval, use LangGraph.
If you need browser control, use Playwright.
If you need retrieval over docs, use LlamaIndex or plain vector search.

Minimal folder setup:
app/
prompts/
tests/
evals/
main.py

Start with one model. Example, GPT-4.1 mini or Claude Sonnet for decent cost vs quality. Save transcripts so you can debug when it does somthing dumb.

Most guides skip evals. That’s the part that matters. Without evals, you’re guessing.

3

I’d actually push back a little on @nachtdromer’s “skip frameworks unless you need branching” rule. That’s mostly right, but for beginners, a tiny framework can sometimes make things clearer, not harder, because it forces you to think in states, inputs, and outputs instead of building a spaghetti loop at 2 a.m.

What helped me was splitting “AI agent” into 4 boring pieces:

  1. Model
  2. Tools
  3. State
  4. Policy

Most tutorials obsess over tools and ignore policy. But policy is the real agent part: when should it call a tool, when should it stop, when should it ask for help.

For a small project, I’d prototype like this:

  • Build one non-agent version first
  • Then add one tool
  • Then add tool-selection logic
  • Then add guardrails

Also: don’t start with memory unless you absolutely need it. Memory sounds cool and usually just creates weird bugs and bad recall lol.

If you want a practical learning path:

  • plain Python + Pydantic for structure
  • FastAPI only if you need an API
  • simple JSON files before a full DB
  • OpenAI/Anthropic SDK directly first
  • then maybe LangGraph if your flow gets messy

Big thing most people miss: define failure cases early. What counts as “wrong”? Hallucinated facts? wrong tool? extra cost? slow replies? If you cant answer that, you’re not building an agent yet, you’re just vibing with an LLM.

4

I’d simplify the decision by choosing your execution environment first, not your framework. That’s where I slightly differ from @nachtdromer. For small projects, the biggest source of pain is often not agent logic, it’s glue code, async issues, env vars, retries, and deployment weirdness.

A practical stack:

  • Python
  • Direct model SDK
  • A tiny tool layer
  • SQLite
  • Structured logs
  • One evaluator script

That last part matters more than people expect. If you cannot replay 20 test prompts and inspect tool calls, you’re basically debugging blind.

My rule: if your agent needs to do fewer than 3 things, don’t call it an agent yet. It’s usually just:

  1. prompt in
  2. optional tool call
  3. formatted answer out

Useful setup:

  • src/agent.py for decision logic
  • src/tools/ for each tool in isolation
  • src/schemas.py for typed inputs/outputs
  • tests/cases.json for regression prompts
  • run_eval.py to score outputs manually or semi-automatically

I also wouldn’t wait too long to add persistence. Not “memory,” just run history. Store prompt, tool calls, result, cost, latency. Pros: easier debugging, clearer failures, better iteration. Cons: more setup, privacy concerns, noisy logs if you over-collect.

Framework pros & cons for a small build:

  • Pros: faster orchestration, less boilerplate, easier multi-step flows
  • Cons: hidden abstractions, harder debugging, version churn, docs often lag reality

So: build the boring observability first, then the agent loop. Most “AI agent development” confusion disappears once you can see exactly why it made a bad call.