Agentic AI Development
Design, build, and deploy production-grade AI agents.
Overview
About this course
A hands-on, end-to-end program that takes you from prompting and RAG to designing, orchestrating, and shipping production-grade AI agents. You will build agents that plan, call tools, use memory, and collaborate, connect them to real systems with the Model Context Protocol, then deploy them safely to the cloud with evaluation, guardrails, and monitoring. Every phase is project-driven and ends in a portfolio-quality capstone.
Who it's for: Developers and data professionals who want to build and ship autonomous AI agents. Comfortable with Python, no prior AI experience required.
What you'll learn
Skills you'll walk away with
- Explain how LLMs, RAG, and agents differ and when to use each
- Build RAG pipelines that ground models in your own data
- Design agent loops with planning, tool calling, and memory
- Connect agents to tools and data with the Model Context Protocol (MCP)
- Orchestrate multi-agent workflows with CrewAI, AutoGen, and LangGraph
- Evaluate, trace, and add guardrails so agents run safely and reliably
- Deploy and monitor agents in production on the cloud
- Ship a portfolio-grade capstone agent from idea to deployment
Curriculum
Your step-by-step learning path
- How AI, ML, deep learning, and generative AI differ from agentic AI
- Large language model families: GPT, Claude, Gemini, and open models like Llama
- Calling model APIs: tokens, context windows, and structured outputs
- Embeddings and vector search with Pinecone, Chroma, and pgvector
- Prompt engineering: zero-shot, few-shot, chain-of-thought, and role prompting
- Retrieval-Augmented Generation (RAG) over documents, SQL, and APIs
- Responsible AI: bias, safety, and ethical use
PythonOpenAI / Claude / Gemini APIsPineconeChromapgvector
Tools & technologies
What you'll work with
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