Build AI Agents with OpenAI SDK: Build Real Agentic AI
Build, debug and deploy real AI agents with the OpenAI Agents SDK — tools, RAG, MCP and multi-agent workflows included.
- Duration
- 2 hours
- Lectures
- 18
- Level
- All Levels
- Learners
- Just launched
- Python
- OpenAI Agents SDK
- Function calling
- RAG
- MCP
- Multi-agent orchestration
About this course
Move beyond single prompts and build real agentic AI. A practical, hands-on course covering the OpenAI Agents SDK end to end — tools, RAG, MCP, multi-agent handoffs, tracing and deployment — so your agents can reason, act and be trusted in production.
What you'll learn
- Build real agents with the OpenAI Agents SDK
- Give agents tools and function calling that actually work
- Ground answers in your own data with RAG
- Connect agents to live systems using MCP
- Orchestrate multi-agent workflows with handoffs
- Debug, trace and deploy agents with confidence
What you'll build
Your first working agent with the OpenAI Agents SDK
A tool-calling agent that takes real actions
A RAG-grounded agent that answers from your own documents
An MCP-connected agent reading live backend data
A multi-agent workflow with handoffs and guardrails
Project architecture
The pipeline you assemble across the course:
- User request
- Agent reasoning loop (OpenAI Agents SDK)
- Tools & function calls
- RAG retrieval over your data
- MCP access to live systems
- Handoff to specialist agent
- Traced, verified response
Curriculum overview
18 lectures · 2 hours of on-demand video.
01Agentic AI foundations
- Why single prompts break down
- Agent loops, state and reasoning
- Setting up the OpenAI Agents SDK in Python
02Tools & function calling
- Defining tools an agent can trust
- Structured inputs and outputs
- Error handling and retries
03RAG & MCP
- Grounding agents in your own data
- Retrieval that improves accuracy
- Exposing live systems safely over MCP
04Multi-agent workflows
- Specialist agents and handoffs
- Guardrails and scope control
- Orchestration patterns that scale
05Debugging & deployment
- Tracing agent runs
- Diagnosing bad reasoning
- Shipping agents to production
FAQ
Do I need prior AI experience?
No. The course starts from agent fundamentals and builds up to multi-agent workflows.
Is this only for QA engineers?
No — any developer building LLM-powered products will use the same patterns, though QA use cases are covered.
What will I have at the end?
Working agents that use tools, RAG, MCP and handoffs, plus a repeatable way to debug and deploy them.
Continue learning
AI Agents
AI Agent: Automated QA Test Case Generator and Executor
Build an AI agent for API test automation using Python, JIRA-API & GenAI
2.5 hours · All Levels
AI Agents
Agentic AI for QA Automation with Python
Design and orchestrate AI-assisted QA agents using Python and AutoGen (2026)
2.5 hours · All Levels
AI Agents
Agentic Workflow Using LangChain and LangGraph
End-to-end agentic workflows: test plans, test cases, scripts & API testing with Python
4 hours · All Levels
Learn more
Ready to start Build AI Agents with OpenAI SDK: Build Real Agentic AI?
Lifetime access, 18 lectures, and a project you can put on your CV.