• views: 76
• date: 25 August 2026
• posted by: AD-TEAM

AI agents can do far more than return a block of text. They can use tools, retrieve live information, collaborate with specialist agents, remember context, ask for human approval, and communicate through voice.
In this hands-on course, you will learn how to build these systems in Python using the OpenAI Agents SDK. We begin with focused lessons covering the core SDK before combining everything into a production-style AI Travel Assistant.
You will learn how to:

Build, configure, run, and trace OpenAI agents

Stream responses and return validated structured outputs

Add function tools, hosted tools, local runtime tools, and MCP servers

Create multi-agent workflows using agents as tools and handoffs

Manage memory, prompts, user context, and self-updating profiles

Add input guardrails, output guardrails, and human approval

Build voice pipelines and continuous realtime voice conversations

Evaluate, red-team, deploy, and monitor an agent application
The main project is a browser-based AI Travel Assistant built with Chainlit. It searches for current weather and flights through MCP, finds activities through web search, remembers user preferences, and delegates work to Weather, Flights, and Places specialists.
You will improve the user experience with live tool-progress updates, customer-support handoffs, reasoning summaries, a rich trip-plan artifact, and realtime voice. You will then protect the application with configurable guardrails and human approval for sensitive actions.
Finally, you will create offline evaluations with Promptfoo, red-team the agent with adversarial inputs, deploy it to Hugging Face Spaces, and monitor production conversations with Langfuse.
The advanced section introduces ChatKit, Sandbox Agents, isolated workspaces, and reusable skills loaded on demand.
This course is designed for developers with basic programming experience. No previous experience with the OpenAI Agents SDK, MCP, multi-agent systems, voice AI, or agent evaluations is required.
By the end, you will have a complete, deployed AI agent project and the practical foundation to design, build, test, and ship your own text and voice agents.

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