Agentic AI Development with LangChain & LangGraph
The production framework stack for building controllable, stateful multi-agent applications.
One-time course fee
Who should take this?
Professionals and developers who need multi-step, tool-using AI systems rather than simple chat interfaces.
Capability developed
Scope, design, evaluate, and govern agents that use tools, memory, state, and human oversight.
Professional application
Research, knowledge, operations, support, or specialised agent implementation.
What’s inside this track
- 1LangChain Fundamentals & LCEL
Build: A basic chain-based Q&A application.
Evidence: Comfortable composing LLM logic declaratively instead of stringing together raw API calls.
- 2Tool Calling & Retrieval-Augmented Generation
Build: A RAG-backed support agent over your own documents.
Evidence: Can ground an LLM in real data reliably instead of hoping it remembers correctly.
- 3LangGraph Orchestration
Build: Convert the Week 2 agent into a stateful LangGraph application.
Evidence: Controls multi-step agent behavior precisely instead of hoping the LLM 'does the right thing.'
- 4Multi-Agent Graphs & Evaluation
Build: Capstone: a multi-agent LangGraph system with full tracing.
Evidence: Ships a LangGraph app with visibility into every decision it makes — not a black box.
What you get
What you will own
Not a promise — a concrete list of what you personally build and can show a buyer, pulled from this course’s real curriculum.
- A working project: A basic chain-based Q&A application.
- A working project: A RAG-backed support agent over your own documents.
- A working project: Convert the Week 2 agent into a stateful LangGraph application.
- A working project: Capstone: a multi-agent LangGraph system with full tracing.
- A completed capstone: Enterprise Support Assistant with Multi-Agent LangGraph, scored across multiple dimensions
- A portfolio entry documenting how you built it, not just that you finished
- Verified skill evidence — tied to real work, not a self-rating
What you’ll prove — and own — by the end
Completion isn’t the finish line. This is what turns the course into evidence you can actually use.
Personalised from day one
A short entry diagnostic flags which modules to move through quickly and which to slow down on — the full curriculum stays available either way.
Evidence, not checkmarks
Skills track through a real mastery ladder — module completed, exercise practiced, project shipped — not just “lesson viewed.”
Your Final real-world project, AI-defended
Enterprise Support Assistant with Multi-Agent LangGraph — submit real work, defend your decisions to an AI interviewer, get scored feedback across multiple dimensions.
A portfolio you can use today
Turn a finished project into a case study, resume bullets, and an interview story — drafted from your real work, yours to review before you use it anywhere.
Connect this course to practical work
See how Ropes links a skill to a business problem, portfolio proof, a bounded service, and responsible client delivery. No client or income outcome is guaranteed.
Earnings potential
A simple illustration of how freelancers commonly structure retainer pricing once they’re taking on client work.
Agent-building retainer
Illustrative example — your results will vary based on niche, effort, and client base.
Industry snapshot
Real, sourced research — current tools, market signal, and named case studies, each with a citation. Not marketing copy.