Building Multi-Agent Systems for Life Sciences

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

Intermediate
12 hours
Updated: Jan 23, 2026

Subscription · Monthly

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Unlimited access to hundreds of top-rated courses
Hands-on projects with expert feedback
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Program Certificates

Skills you'll learn

13 skills

Multi-Agent RAG Concepts
Multi-Agent Agent Orchestration Concepts
Implementing Multi-Agent RAG
Multi-Agent System Design
Implementing Multi-Agent Orchestration

Prerequisites

4 prerequisites

Prior to enrolling, you should have the following knowledge:

Implementing Agent Tool Use
Implementing Agent Memory
Implementing Database Interaction for AI Agents
Implementing API Integration for Agents

You will also need to be able to communicate fluently and professionally in written and spoken English.

Course Outline

16 lessons
1 project

Building Multi-Agent Systems for Life Sciences

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.

Program Instructors

2 instructors

Unlike typical professors, our instructors come from Fortune 500 and Global 2000 companies and have demonstrated leadership and expertise in their professions:

Tamas Madl

Healthcare & life sciences AI leader

Christopher Agostino

Founder and Research Scientist at NPC Worldwide

Tamas Madl

Healthcare & life sciences AI leader

Christopher Agostino

Founder and Research Scientist at NPC Worldwide

About this program

Master multi-agent solutions in life sciences. Orchestrate agents to retrieve, validate, and synthesize data into actionable biomedical dossiers.

Subscription · Monthly

Cancel Anytime
Unlimited access to hundreds of top-rated courses
Hands-on projects with expert feedback
Personalized career coaching and interview prep
Program Certificates

Building Multi-Agent Systems for Life Sciences

Skills you'll learn

Prerequisites

Course Outline

Program Instructors

About this program

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