From Modular Design to AI Agents – Rethinking Software for Lab Automation

AI agents are transforming how labs connect data, software, and automation systems — but implementing them successfully requires more than experimentation. This course introduces a practical framework for moving from modular, rule-based systems to intelligent, agentic architectures that are secure, scalable, and enterprise-ready.

Drawing from real-world examples in life sciences R&D, instructors will guide participants through the evolution of AI agents — from simple workflow assistants to multi-agent systems that can orchestrate complex lab operations. Attendees will learn how to identify high-value opportunities for agent use, prototype proof-of-concept (POC) agents using new SDKs and Model Context Protocol (MCP) integrations, and evaluate readiness for production deployment.

The session will be highly interactive, combining discussion, live demonstrations, and guided exercises that culminate in attendees building their own agent POC using provided frameworks and open tools.

Who Should Attend?

• Scientists, engineers, and informatics specialists exploring AI-driven automation
• Laboratory automation professionals integrating ELNs, LIMS, and scheduling systems
• R&D IT and digital transformation leaders planning enterprise AI adoption
• Software developers and system architects supporting lab informatics or automation

Course Benefits

• Understand the evolution from modular software to agentic systems in laboratory environments
• Learn a framework for identifying, scoping, and validating AI agent use cases
• Prototype AI agents using modern SDKs and open standards like MCP
• Recognize security, validation, and scalability requirements for enterprise-grade AI
• Leave with a practical reference framework and a working agent POC to build on after the course

Course Topics

• Introduction: Modular to Agentic Systems:  Why modular design paved the way for agentic architectures
• Understanding Agent Types: From task-based assistants to multi-agent orchestration across systems
• Framework for Evaluating Use Cases: Identifying ROI, feasibility, and scientific integrity checkpoints
• Building Agents Step-by-Step: From workflow mapping to prototyping using MCP and SDKs
• Demo: Building and Running Agents:  Hands-on examples including background monitoring, chemistry agent, and research agent POCs
• Enterprise-Readiness:  Addressing compliance, security, and data governance for regulated environments

Instructors

Bishoy Youssef
Integrant, Inc.

With over two decades of experience in software engineering, Bishoy Youssef specializes in designing intelligent systems that simplify complex scientific and automation workflows. As a Senior Solutions Architect, he focuses on building practical, modular and agentic tools that integrate seamlessly with the way scientists and engineers work.

Youssef is one of Integrant’s leading experts in AI and agentic architectures, contributing to the development of MCP-based integrations, multi-agent frameworks and advanced prototypes used across chemistry, biology and research informatics. His approach emphasizes collaboration, experimentation, and problem-solving — enabling teams to rapidly prototype AI agents while maintaining the reliability, security and scientific rigor required in enterprise lab environments.

Yousef Awad
Integrant, Inc.

Yousef Awad is the CEO of Integrant, Inc., a global software engineering partner specializing in AI-powered and agentic systems for life sciences and manufacturing. He leads a 300+ engineer organization that builds secure, scalable software for enterprise R&D environments.

A data architect by training, Awad has spent more than three decades bridging software engineering and applied science, helping organizations evolve from modular architectures to intelligent, AI-driven systems. Under his leadership, Integrant’s teams have delivered solutions supporting digital transformation for biotech, medical device and manufacturing innovators worldwide. His current focus is on advancing enterprise-ready agentic frameworks that enable labs to prototype and scale AI safely, securely, and effectively.