Overview

This course focuses on designing, implementing, and orchestrating multi-agent architectures. Starting with an introduction to the fundamentals, participants will learn the nuances of building multi-agent systems using Python. Key lessons cover agent orchestration, routing and data flow management, and state management within these systems. Practical implementations will guide students through developing sophisticated multi-agent orchestration and coordination strategies. The course also explores advanced topics such as Multi-Agent Retrieval Augmented Generation and culminates with a project on the Orphan Finder, a rare-disease variant-to-therapy matchmaker.

Syllabus

Course Introduction

Meet your instructors and get started with building multi-agent AI systems, learning architecture, orchestration, and using Vocareum OpenAI API keys for hands-on projects.

Designing Multi-Agent Architecture

Explain the core components of multi-agent systems and how to design their high-level architecture.

Creating Multi-Agent Designs

Learn to build a multi-agent AI system with an orchestrator that routes genomics queries to specialist agents using real APIs for frequency, significance, literature, and trials.

Multi-Agent Architecture with Python

Develop a multi-agent system by coding the designed architecture and connecting agents with well-defined interfaces.

Implementing Multi-Agent Architecture with Python

Learn to design and implement multi-agent architectures in Python, integrating specialists with orchestrated logic and API tools for real-world life sciences workflows.

Orchestrating Agent Activities

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

Implementing Agent Orchestration

Learn to build stateful agent workflows using sequential, parallel, and conditional orchestration for drug-target analysis and service desk automation in life sciences.

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

Learn to design agentic systems that use LLMs and priority queues for scalable content-based and priority-based routing in real-world scenarios.

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

Learn how to manage shared state in multi-agent systems through demos and exercises, enabling coordination between agents for collaborative tasks using thread-safe designs.

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

Learn multi-agent orchestration by coordinating access to shared lab resources, preventing conflicts through atomic state updates and locks, with priority scheduling of concurrent bookings.

Multi-Agent Retrieval Augmented Generation

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

Implementing Multi-Agent Retrieval Augmented Generation

Learn how to build Multi-Agent RAG systems that retrieve domain-specific evidence in parallel and synthesize concise, cited reports for clinical or scientific queries.

Project: Orphan Finder: Rare‑Disease Variant‑to‑Therapy Matchmaker

In this project, you will build a compact multi-agent workflow (3 agents) that ranks variants, pulls research-backed evidence, finds clinical trial matches, and outputs a clinician-ready report.