Your first AI agent should not be a do-everything assistant. Build a small agent that solves one real problem, with narrow tools and obvious failure limits.

A good starter project is a research summarizer:

  1. Take a topic.
  2. Search approved sources.
  3. Read a few pages.
  4. Summarize findings.
  5. Cite sources.
  6. Stop.

That teaches the core agent pattern without giving the system dangerous powers.

What An Agent Needs

At minimum, an agent needs:

  • A model.
  • Instructions.
  • Tools.
  • State.
  • A loop or runner.
  • Stop conditions.
  • Logging.

A tool can be a search function, file reader, database query, ticket lookup, calculator, browser action, or internal API. The model decides when to use the tool, but your software decides what the tool is allowed to do.

Choose The Right Build Path

Use a direct API call if the workflow is simple and mostly linear.

Use OpenAI Agents SDK if you are building with OpenAI models and want built-in concepts such as agents, tools, handoffs, guardrails, sessions, and tracing.

Use LangGraph if you need explicit state, branches, loops, durable execution, streaming, or human-in-the-loop checkpoints.

Use CrewAI if your workflow naturally maps to multiple roles, such as researcher, writer, reviewer, and coordinator.

Do not use a framework just to feel advanced. If a normal function and one model call solve the job, use that.

Define The Agent Contract

Before code, write this down:

Agent name:
Research Summarizer

Goal:
Create a sourced summary of a topic from approved web sources.

Allowed tools:
- search_web
- read_url
- summarize_source

Not allowed:
- send email
- write files
- purchase anything
- access private customer records

Stop conditions:
- 5 search results reviewed
- 3 reliable sources summarized
- 10 tool calls reached
- source quality too low

Human review:
Required before publishing or sending externally.

This contract prevents scope creep.

Design Tools Carefully

Good tools have small, typed inputs and predictable outputs.

Bad tool:

do_anything(input)

Better tools:

search_web(query, allowed_domains)
read_url(url)
extract_claims(text)
summarize_sources(source_notes)

The model should not receive unlimited power through one vague tool.

Track State

State lets the agent know what it has already done.

Useful state fields:

  • User goal.
  • Search queries tried.
  • URLs read.
  • Source notes.
  • Claims extracted.
  • Errors.
  • Tool-call count.
  • Current step.
  • Final answer.

Without state, agents repeat themselves, lose context, or stop too early.

Add Guardrails Early

Guardrails are easier to add at the beginning than after the first failure.

Include:

  • Maximum runtime.
  • Maximum tool calls.
  • Maximum spend.
  • Allowed domains or data sources.
  • Human approval for external actions.
  • Refusal when sources are insufficient.
  • Logging of every tool call.

For your first agent, avoid write actions entirely. Read-only agents are safer and easier to debug.

Evaluate The Agent

Create 20 to 30 test tasks before calling the agent useful.

Check:

  • Did it choose relevant sources?
  • Did it ignore unreliable sources?
  • Did it cite claims correctly?
  • Did it stop when information was missing?
  • Did it stay within tool limits?
  • Did it produce a usable output?
  • Did it avoid unsupported claims?

Agent evaluation is not just “does the final answer look good?” You need to inspect the steps.

A Minimal Agent Flow

Use this as the first version:

Input topic
-> plan search queries
-> search approved sources
-> read top results
-> extract supported claims
-> draft summary with citations
-> run final review
-> return answer and source list

Only add memory, multiple agents, write actions, or scheduling after this version works reliably.

Common Mistakes

The first mistake is giving the agent too many tools. Start with two or three.

The second mistake is skipping stop conditions. Every loop needs a hard end.

The third mistake is letting the agent act externally before it has proven reliable internally.

The fourth mistake is evaluating only the final output. Inspect the trace.

The fifth mistake is using agents where a deterministic workflow would be better.

Bottom Line

Build your first agent like a small production system, not a toy demo. Give it one job, narrow tools, state, limits, logs, and a human review path.

Once that works, expand carefully.

Verified Sources