Sam is Teaching us How to Build AI Agents!

OpenAI - A Practical Guide to Building Agents ⚡

Apr 20, 2025

We’ve been talking about AI Agents for weeks! how they’re changing workflows, how to build them, how they’re not just smart chatbots... but now?

OpenAI pulled up with a 34-page PDF that breaks it down. no hype, no marketing talk. Just pure, practical instructions for teams building AI agents powered by LLMs.

The name?

**A Practical Guide to Building Agents by OpenAI**

Straight to the point. Actionable. Technical. And honestly? Really helpful.

Let’s break it down 👇

First, What Even Is an “Agent”?

If you still think agents are just smarter chatbots, this guide makes it clear:

"Agents are systems that independently accomplish tasks on behalf of users."

They’re powered by LLMs, can use tools, make decisions, and actually finish a workflow. Like refunding an order, booking travel, updating databases, or writing reports.

So no, your vanilla chatbot that answers FAQs? That’s not an agent.
But a tool-connected LLM that handles a real user goal, is.

When Should You Actually Build One?

Don’t just build agents for the sake of it.

OpenAI gives 3 clear signals that say “this needs an agent”:

Complex decisions
Like refund approvals or onboarding logic.
Messy rule-based systems
Like security reviews or contract workflows.
Tons of unstructured data
Think insurance claims, PDF parsing, or customer emails.

If you're checking one of those boxes? It’s agent time.

Agent Building Blocks: Keep It Simple

Every agent has 3 parts:

Model: The brain (reasoning, decision-making)
Tools: The hands (APIs, actions, web access, etc.)
Instructions: The map (what to do, when, and how)

And they give you Python SDK examples to actually wire this up.

Check it Out ❤️

What Model Should You Use?

Their advice is gold:
Start with the most capable model. Then swap in smaller ones once you know what works.

Here’s the flow:

Prototype with GPT-4 or o3.
For smaller tasks (like classification, retrieval), use mini models (o1, o4-mini).
Let the big model handle reasoning; let the small ones save costs.

And yes, OpenAI says you can even use a smaller model to write instructions for your agent. Meta.

Tools = Power Moves

Agents become 100x more useful when they can:

Look stuff up
Take action
Coordinate with other agents

They outline 3 types of tools:

Data Tools: like web search, reading docs
Action Tools: like sending emails, updating CRMs
Orchestration Tools: where one agent calls another

⚡ Pro tip: If your agent has too many tools, it’s time to split into multiple agents. (More on that below.)

Instructions Are Underrated

Good agents aren’t vague.

OpenAI pushes clear, structured instructions. And shows how to auto-generate them from help docs using models.

Tips from the guide:

Use real process docs as your base
Turn them into numbered steps
Handle edge cases directly
Be explicit in what the agent should do

They even give you a prompt to turn policy docs into agent instructions. Copy/paste ready.

Agent Orchestration: One or Many?

This part’s huge.

There are two main patterns for scaling agent logic:

Single-Agent Systems

One agent with tools and logic
Good for most MVPs
Use prompt templates to keep it clean
Think: Customer service assistant, knowledge bot, refund processor

Multi-Agent Systems

When you need more specialization or logic branching, go multi-agent.

Two patterns here:

Manager Pattern:

The manager pattern empowers a central LLM—the “manager”—to orchestrate a network of specialized agents seamlessly through tool calls. Instead of losing context or control, the manager intelligently delegates tasks to the right agent at the right time, effortlessly synthesizing the results into a cohesive interaction. This ensures a smooth, unified user experience, with specialized capabilities always available on-demand. This pattern is ideal for workflows where you only want one agent to control workflow execution and have access to the user.

Decentralized Pattern:

In a decentralized pattern, agents can ‘handoff’ workflow execution to one another. Handoffs are a one way transfer that allow an agent to delegate to another agent. In the Agents SDK, a handoff is a type of tool, or function. If an agent calls a handoff function, we immediately start execution on that new agent that was handed off to while also transferring the latest conversation state. This pattern involves using many agents on equal footing, where one agent can directly hand off control of the workflow to another agent. This is optimal when you don’t need a single agent maintaining central control or synthesis—instead allowing each agent to take over execution and interact with the user as needed.

They give full code examples for both in the guide. From customer service to translation systems.

Guardrails = Your Agent’s Seatbelt

You don’t want your agent:

Going off-topic
Spilling private info
Taking high-risk actions unprompted

OpenAI gives a full system for guardrails:

- Relevance Classifier
- Safety Classifier
- PII Filter
- Moderation (via OpenAI API)
- Tool Risk Levels (with human escalation options)

⚡ Plus: you can run all of these inline with your agent’s run loop, and even test for failures automatically.

Human-in-the-Loop: Yes, You Still Need It

OpenAI says it plain:
Agents should hand off to humans when:

They’re failing (after retries)
The action is sensitive (e.g., issuing refunds, canceling accounts)

No matter how good your model is, don’t skip this.

Final Takeaway: This Is the New Playbook

OpenAI’s guide is A great blueprint that we’ve got right now for building real agents. The kind that aren’t just cool demos, but actually work in production.

If you're building:

Customer support flows
Internal tools
LLM-powered assistants
Any kind of agent-like system

You owe it to yourself to read this guide. It’s clear, it’s technical, and it’s 100% actionable.

You can have it for yourself:

A Practical Guide to Building Agents ❤️

If you liked this issue of AI Agents Simplified, share it with your friends and spread the knowledge! ❣️