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.
- 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.
