Mastering AI Agents with the OpenAI Agents SDK

About this course

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 s

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