Mastering AI Agents with the OpenAI Agents SDK

Build real, tool-using AI agents with the OpenAI Agents SDK — from design to triage, handoffs, guardrails, and eval.

What you'll learn
- Design a clear mental model of Agentic AI and explain how it differs from code-driven and LLM-only approaches.
- Build working agents using the OpenAI Agents SDK: configure models, tools, handoffs, hooks, guardrails, and context/memory.
- Implement the Agent Loop (Observe → Think/Plan → Act → Reflect) and constrain it for reliable, auditable behavior.
- Create real tools (Python functions) for external systems and ensure the agent performs side-effecting work only via tools.
- Orchestrate multi-agent workflows (triage → specialists), including handoff hooks to pass state safely.
- Add evaluation (eval) harnesses that verify routing, tool usage, confirmations, and safety—and catch regressions automatically.
- Run agents with both OpenAI-hosted models and local/OpenAI-compatible backends (e.g., vLLM/Qwen in Colab), swapping endpoints without code changes.
- Ship a small front end (Gradio) and basic observability (traces/logs), turning a notebook prototype into a demo-ready application.

Requirements
- Basics of Python
- Basics of LLM

Description
Build AI agents that don’t just talk — they act.

Most teams stop at “ChatGPT inside a UI.” That’s not an agent.

This course teaches you how to design, build, and evaluate real AI agents using the OpenAI Agents SDK. You’ll learn how to give models tools, memory, policy boundaries, and the ability to route work — so they can solve real problems like customer support, operations, and workflow automation.

Instead of slideware, we build a full end-to-end system together: an airline customer support assistant that can route requests, answer policy questions, change seats, handle cancellations and reschedules, respect business rules, and escalate when needed. You’ll also learn how to test it, monitor it, and plug it into a simple UI.

What you’ll learn

The difference between traditional automation, LLM “assistants,” and true agentic systems — and when you should use which.

How to decide if your use case actually needs an agent (or if a single LLM call is enough).

How the Agent Loop works (Observe → Think/Plan → Act → Reflect) and how to force the model to operate step-by-step instead of hallucinating.

How to expose real tools (Python functions / APIs) to the model so it can look up data, take actions, and update systems — safely.

How to build multi-agent systems with triage and handoffs (for example: FAQ agent, seat booking agent, cancellation and rebooking agent).

How to attach guardrails for policy, confirmation, and safety.

How to persist context and memory across turns.

How to evaluate the agent’s behavior automatically — routing, tool usage, escalation, and confirmation gates — using a lightweight JSONL eval harness.

How to run your agent against both OpenAI models and OpenAI-compatible local models (e.g. self-hosted / vLLM / Qwen-style endpoints).

How to wrap it in a front end (Gradio) so stakeholders can try it like a real support assistant.

How the course flows

1. Mindset and architectureWe start by building a mental model of Agentic AI. You’ll learn what an AI agent actually is in practical terms: a system that can observe what’s happening, plan the next step, call tools to act, and reflect on whether it’s done — not just answer in plain text. We compare code-driven automation, pure “chatbot-style” LLMs, and agentic designs.

We also walk through a 5-question test you can use with any new idea to decide: “Is this really an agent use case, or am I overcomplicating it?”

2. Core building blocks of the OpenAI Agents SDKYou’ll get hands-on with the SDK primitives you’ll use every day:

Agent: the brain with instructions and role.

Runner: the execution engine that drives turns.

Tools: Python functions the model can call to get facts or take actions.

Handoffs: how one agent can delegate to a more specialized agent.

Hooks: how to inject state or perform setup logic right when a handoff happens.

Guardrails: how to block unsafe input or enforce policy around output.

Context & memory: how the agent carries knowledge through a conversation.

The Agent Loop (Observe → Think/Plan → Act → Reflect): how to orchestrate multi-step work.

You’ll see minimal “hello world” versions of each concept and then gradually stack them into something production-like.

3. Real project: Airline Customer Support AgentNext, we build a working multi-agent support system. This includes:

A Triage Agent that decides what the user is asking and routes to the right specialist.

An FAQ Agent that answers policy questions from a controlled knowledge base.

A Seat Booking Agent that can take a confirmation number and change a seat — via a tool call, not just text.

A Cancellation & Rescheduling Agent that can fetch fare rules, present rebooking options, and apply cancellation policy safely (with explicit confirmation before doing anything irreversible).

You’ll learn how agents hand off to each other, how they populate context (like confirmation numbers or flight IDs), and how guardrails prevent them from doing unauthorized work.

4. Observability and UI demoWe build a tiny front end in Gradio so you (and your stakeholders) can chat with the system like a real airline support bot. You’ll also see how to log tool calls, handoffs, and decisions for debugging and auditability.

5. Evaluation and safetyFinally, you’ll learn how to test agents like software. We’ll build a small evaluation harness that:

feeds in realistic customer prompts,

checks whether the right agent took control,

verifies that the correct tools were called (and forbidden tools were not),

enforces that certain actions require confirmation.

We’ll generate a pass/fail scorecard so you can see if you broke routing, safety, or policy after making changes. This is the difference between “cool demo” and “something you could actually deploy.”

Who this course is for

AI/ML engineers and LLM developers who want to go beyond chat interfaces.

Backend / platform / ops engineers who want safe, automatable agents that touch real systems.

Product engineers / founders building internal copilots and support bots.

Technical leads who need to evaluate whether “agentic AI” is real or hype.

You should be comfortable with basic Python. You donotneed to be a deep learning researcher.

Why this matters

Most companies are about to wire LLMs into critical workflows — support, operations, monitoring, onboarding, billing. Doing that safely requires more than a prompt. It requires agents that have roles, memory, tools, guardrails, escalation, and evaluation.

By the end of this course, you’ll know how to build that. And you’ll walk away with working, inspectable code you can adapt to your own use case.

Who this course is for:
- AI/ML engineers and LLM developers who already know how to call an LLM API (OpenAI, local models, etc.) and now want to build multi-step, tool-using, policy-aware agents.
- Backend / platform / automation engineers who currently build internal tools or ops automations and want to layer intelligent triage, decision-making, and self-service on top of existing systems.
- Founders and product engineers working on AI products (support copilots, monitoring/ops assistants, internal copilots) who need to understand how to safely wire LLMs to business logic, APIs, and data.
- Technical team leads / architects evaluating “agentic AI” claims and looking for a practical, observable, testable architecture that won’t blow up in production.
More Info