Build the skills to understand, design, evaluate, and deploy AI agents tailored to financial workflows.
This hands-on program gives you the foundation, tools, and practical experience to master agentic systems.
We start from the fundamentals and guide you to an intermediate-advanced level, equipping you to build full end‑to‑end agentic systems for real finance applications.
It’s ideal if you are:
A financial analyst, trader, portfolio manager, or researcher looking to automate analysis and decision‑support workflows with AI.
A finance leader or product owner who wants to understand how agentic systems can transform processes and create new opportunities.
A consultant or innovation manager exploring how to integrate AI agents into client offerings.
Data scientists, ML engineers, and software developers interested in building advanced AI agents.
To get the most out of the course, you should:
Have a basic understanding of financial data and workflows:
Be comfortable with basic computer skills and willing to use Python notebooks with starter code we provide. (No advanced coding skills necessary; we guide you through everything.)
Have used tools like ChatGPT or another AI‑powered assistant at least once so you’re familiar with the concept of prompts and responses.
If you’ve never built anything with AI before – that’s okay. Week 1 will bring everyone to the same level before we dive deeper.
By the end of the course, you will be able to:
Understand when and why to use AI agents over traditional single‑prompt or RAG‑only solutions.
Be familiar with the different design patterns in agentic systems and get hands‑on to master the underlying concepts.
Build AI agents using reasoning paradigms like ReAct, Reflection through hands-on labs and get introduced to more advanced approaches such as ToT, LATS.
Learn the different architectural styles in multi-agent collaboration systems (e.g. Orchestrator‑Worker, Hierarchical, Peer‑to‑Peer) and build them.
Work with leading frameworks (LangGraph, LlamaIndex, OpenAI Agents SDK, Google ADK, CrewAI, AutoGen, SmolAgents) and understand how to choose the right one for your needs.
Instrument and evaluate agents with tracing, observability, and evaluation techniques.
Build a full end‑to‑end multi-agent system for financial research or analysis.
Deliver a full end‑to‑end finance agent as your capstone project.
Week 1 – Foundations of LLMs & Why Agents
Week 2 – AI Agents 101
Week 3 – Agentic Patterns
Week 4 – Frameworks Deep Dive
Week 5 – Reasoning Paradigms (Practical)
Week 6 – Architectures of Multi-Agent Systems & Memory
Week 7 – Guardrails, Tracing & Evaluation
Week 8 – End-To-End Multi-Agent Finance System – Pratical Projects
Week 9 – 10 – Present Your Project
👉 Reserve your spot now and learn how to build AI agents in finance