getting started with ai agents

Building an AI agent sounds abstract until you ship one. This guide walks through the decisions that actually matter when you’re starting out — not the theory, but the stack choices and patterns that get you to a working prototype fast.

what counts as an agent

An agent is a loop: the model receives a goal, decides what to do, calls tools, observes results, and repeats until the job is done. The difference from a chatbot is action — agents don’t just respond, they execute.

If your system can search the web, read files, call APIs, or run code based on model decisions, you’re building an agent.

pick your runtime first

Before prompts or tools, choose where the loop runs:

• Framework-first (LangGraph, CrewAI, AutoGen): good when you need orchestration, memory, and multi-agent patterns out of the box.
• SDK-first (OpenAI Agents SDK, Anthropic tool use): good when you want minimal abstraction and direct model control.
• Roll your own: a while loop with tool definitions and structured outputs. Often the fastest path for a v1.

Start with the smallest runtime that supports your use case. You can always migrate once you know what breaks.

define tools narrowly

Each tool should do one thing clearly. Vague tools confuse the model; focused tools get called correctly.

Good tool: search_docs(query: string) → relevant paragraphs from your knowledge base

Bad tool: do_research(task: string) → everything about the topic

Write tool descriptions as if you’re onboarding a junior engineer. Include when to use the tool, what inputs mean, and what the output looks like.

ship a vertical slice

Don’t build the full agent on day one. Pick one workflow end-to-end:

1. User asks a question
2. Agent calls one tool
3. Agent returns a useful answer

Get that working, demo it, then add memory, more tools, and error handling. The builders who ship fastest treat agents like any other product — smallest useful version first.

common failure modes

• Too many tools upfront — the model picks wrong or loops forever. Add tools one at a time.
• No observability — you can’t debug what you can’t see. Log every tool call and model turn from day one.
• Prompt as architecture — if your prompt is 2,000 tokens of rules, extract logic into code and keep the prompt focused on behavior.

next steps

Download the checklist and tool comparison sheet below. Use them to scope your first agent build, then bring what you ship to the next weekly demo.