Leveraging Software Design Principals for Multi-Agent AI Systems.

This talk examines practical strategies for leveraging AI through precise prompting, leveraging software-inspired design, and enterprise scalability. I share my path from analytics to building 4,000+ GPTs after discovering AI could automate a significant of my work. Using a few different lessons, I illustrate how explicit instructions transform outputs. I apply object-oriented principles—encapsulation, abstraction, inheritance, polymorphism—elevates prompts into modular, reusable agents. At scale, success comes from auditable prompt libraries, strict guardrails, micro-prompts, and feedback loops. Key takeaway: you can contextualize AI experiences in a very flexible manner producing more reliable, consistent, and better performing agents to support business criteria.
Who is this presentation for?
Technical and business users
Prerequisite knowledge:
none required
What you'll learn?

Jack Martin introduces AI-first design principles for building modular, reusable, and scalable agents powered by dynamically contextualized content and data. He explores how multi-agent and orchestrated AI workflows can transform enterprise operations. Drawing on his experience building and scaling an AI program from zero to 3,500 users in nine months, and developing more than 4,000 GPTs, for Ai-GEN.co, Jack shares practical strategies for leveraging advanced prompting methodologies, and software design to deploy generative AI effectively at scale.