Overview

Master the architecture of Multi-Agent AI for finance. You will move beyond simple chatbots to build specialized agent teams that collaborate on high-stakes workflows like OTC trading. Using Python and PydanticAI, you will learn to implement orchestration, intelligent routing, and persistent state management to create immutable audit trails. You will master the critical balance of blending non-deterministic AI reasoning with deterministic compliance logic. In the course project, "Agentic Alpha," you will architect a fully functional, autonomous trading system that analyzes markets, calculates risk, and enforces regulatory rules in real-time.

Syllabus

Introduction to Multi-Agent AI Systems for Financial Services

Learn about the course, prerequisites, and technical environment.

Designing Multi-Agent Architecture for Financial Services

Explore how specialized agents collaborate in a multi-agent AI architecture for hedge funds, focusing on data, strategy, risk, compliance, and trade execution flows.

Implementing Multi-Agent Architecture with Python for Financial Services

Learn to implement multi-agent architectures in Python for financial services using Pydantic AI, shared context, clear agent roles, and strict output structures for robust communication.

Orchestrating Agent Activities

Apply orchestration techniques to coordinate multiple agent actions and achieve complex workflows.

Implementing Agent Orchestration for Financial Services

Learn how to implement agent orchestration in financial services by combining deterministic flows with AI-powered decision making, using orchestration patterns and structured output models.

Routing and Data Flow in Agentic Systems

Configure routing mechanisms to manage data flow among agents in multi-agent systems.

Implementing Routing and Data Flow in Agentic Systems for Financial Services

Learn to implement routing strategies, agent pooling, and data flow orchestration for multi-agent financial systems using LLM and classic methods to balance loads and optimize message handling.

State Management in Multi-Agent Systems

Evaluate methods for tracking and updating agent state across multi-turn interactions.

Implementing State Management in Multi-Agent Systems for Financial Services

Learn to implement persistent, auditable state management in multi-agent trading systems using CSV files, ensuring consistent, reliable execution and separation of decision and action.

Multi-Agent Orchestration and State Coordination

Develop a coordinated multi-agent system that synchronizes states for coherent task execution.

Implementing Multi-Agent Orchestration and State Coordination for Financial Services

Learn to coordinate multiple specialist AI agents for financial trade processing using state management, conflict detection, and atomic transactions for robust multi-agent orchestration.

Multi-Agent Retrieval Augmented Generation

Extend RAG to multiple cooperating agents, each specialized in certain retrieval tasks.

Implementing Multi-Agent Retrieval Augmented Generation for Financial Services

Learn how to build a Multi-Agent Retrieval-Augmented Generation system for finance, coordinating specialized agents for comprehensive trading analysis and recommendations.