Course

The AI Agents Bootcamp : From Prompts to Production

Business Analytics Institute

Delivered AI lessons to 700k+ learners

AI Is Going Agentic. Build & Deploy the Multi-Agent Systems that Companies Need

You've built a chatbot. Maybe a RAG demo. But when it's time to ship something real - an agent that reasons, retrieves, acts, and recovers from failure - the tutorials stop helping. Demos break in production. Hallucinations slip past. Costs balloon. And "just add an agent framework" doesn't answer the hard questions: When do you need multiple agents? How do they share memory and tools safely? How do you even measure if it's working?

That's the gap this bootcamp closes.

The industry is shifting fast from single-prompt LLM apps to orchestrated, protocol-driven agent systems - and companies are hiring for engineers who can build them, not just talk about them. In live, hands-on sessions, you'll build reflection loops, planning flows, and multi-agent teams in LangGraph; design hybrid retrieval with vector databases; wire agents to real tools through MCP; and evaluate everything with frameworks like RAGAs and G-Eval.

You won't just learn agentic AI. You'll leave with a production-ready multi-agent application you built yourself - and the judgment to know what to build next.

What you’ll learn

Build, evaluate, & ship multi-agent AI systems with LangGraph, RAG & MCP - skills companies are hiring for right now.

  • Work through structured project phases, from idea to prototype, using step-by-step implementation guides and ready-to-use code templates

  • Choose a real-world project track: virtual assistant, marketing co-pilot, legal & compliance assistant, IRS tax Q&A bot, or an MCP chatbot.

  • Incorporate version control, automated testing, and monitoring habits so your app is reliable and continuously improvable

  • Master LangGraph fundamentals - nodes, edges, states, and condition-based execution - through live hands-on labs

  • Build supervisor agents, hierarchical delegation flows, and approval nodes with human-in-the-loop checkpoints

  • Apply Tree of Thoughts (ToT) and DAG-based planning for structured, parallel reasoning across agent teams

  • Implement hybrid search combining dense and sparse vectors, metadata filters, and full-text tokenization in AI-native vector databases

  • Apply re-ranking with Maximal Marginal Relevance (MMR), semantic caching with TTL policies, and HNSW index health monitoring in guided labs

  • Design memory tiers - short-term, episodic, and long-term - so agents use vector DBs as reliable long-term memory

  • Build an MCP client in Streamlit: connect to servers, discover available tools, and automate workflows that retrieve and validate data

  • Compare MCP, A2A, and ACP protocols so you can choose the right interoperability standard for any workflow

  • Learn MCP security essentials - permissions, auth, rate limits, audit trails, to deploy protocol-driven agents safely in enterprise setting

  • Implement each core pattern - Reflection, Planning, Tool Use, and Multi-Agent Collaboration - in dedicated hands-on labs

  • Compose patterns into hybrid workflows, weighing trade-offs between autonomy and control, flexibility and stability

  • Study patterns as a reusable pattern language, so you can recognize and apply the right structure to any new problem

  • Apply RAGAs to score faithfulness, answer relevance, and context precision on retrieval + generation pipelines

  • Compare BLEU, ROUGE, and BERTScore on generated text, and use G-Eval for open-ended agent outputs

  • Track production-grade observability metrics — latency, recall, throughput, and drift — and benchmark against MMLU, HELM, and HotpotQA

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Who this course is for

  • Data scientists & ML engineers who've built LLM demos but need to ship production-grade, orchestrated agent systems.

  • Tech leads & architects deciding when to use multi-agent systems, which frameworks to adopt, and how to govern them.

  • PMs, analysts & founders with basic Python who want hands-on agentic AI skills to lead or launch AI initiatives credibly.

Prerequisites

  • Basic Python Programming

    Comfort writing and running Python scripts - functions, loops, and installing packages. No advanced engineering needed.

  • Familiarity with LLMs like ChatGPT or Claude

    You've used LLMs and understand prompting basics. Prior API experience helps. Parse JSON responses enough to connect tools and data sources

  • Curiosity & Willingness to Build Hands-On

    Every module includes live labs. Come ready to write code, break things, and debug - that's where the real learning happens.

What's included

Lifetime access

Go back to course content and recordings whenever you need to.

Community of peers

Stay accountable and share insights with like-minded professionals.

Certificate of completion

Share your new skills with your employer or on LinkedIn.

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