Michael Carducci spent years learning to see things as they actually are; first as a magician, then as a software architect, now as both simultaneously. And somehow that’s not even the whole story.
He’s the author of Mastering Software Architecture (Apress, 2025) and is currently writing The Semantic Layer. He has spent over 25 years following interesting problems; through roles from individual contributor to CTO and back again, across industries and continents.
As a speaker, he applies the same toolkit he uses in close-up magic: attention, misdirection, timing, storytelling, and the instinct to take the long way around when that’s where the truth lives. Audiences at hundreds of conferences across four continents have described his talks as the kind that change how you think about a problem rather than just what you know about it.
He also makes YouTube videos about technology and curiosity with his wife Kate, because some ideas are too important (or too interesting!) to leave only in conference rooms.
The age of hypermedia-driven APIs is finally upon us, and it’s unlocking a radical new future for AI agents. By combining the power of the Hydra linked-data vocabulary with semantic payloads, APIs can become fully self-describing and consumable by intelligent agents, paving the way for a new class of autonomous systems. In this session, we’ll explore how mature REST APIs (level 3) open up groundbreaking possibilities for agentic systems, where AI agents can perform complex tasks without human intervention.
You’ll learn how language models can understand and interact with hypermedia-driven APIs, and how linked data can power autonomous decision-making. We’ll also examine real-world use cases where AI agents use these advanced APIs to transform industries—from e-commerce to enterprise software. If you’re ready to explore the future of AI-driven systems and how hypermedia APIs are the key to unlocking it, this session will give you the knowledge and tools to get started.
REST APIs often fall into a cycle of constant refactoring and rewrites, leading to wasted time, technical debt, and endless rework. This is especially difficult when you don't control the API clients.
But what if this could be your last major API refactor? In this session, we’ll dive into strategies for designing and refactoring REST APIs with long-term sustainability in mind—ensuring that your next refactor sets you up for the future.
You’ll learn how to design APIs that can adapt to changing business requirements and scale effectively without requiring constant rewrites. We’ll explore principles like extensibility, versioning, and decoupling, all aimed at future-proofing your API while keeping backward compatibility intact. Along the way, we’ll examine real-world examples of incremental API refactoring, where breaking the cycle of endless rewrites is possible.
This session is perfect for API developers, architects, and tech leads who are ready to stop chasing their tails and want to invest in designing APIs that will stand the test of time—so they can focus on building great features instead of constantly rewriting code.
Integration, once a luxury, is now a necessity. Doing this well, however, continues to be elusive. Early attempts to build better distributed systems such as DCOM, CORBA, and SOAP were widely regarded as failures. Today the focus is on REST, RPC, and graphql style APIs.
Which is best? The goto answer for architects is, of course, “it depends.”
In this session, we look at the various API approaches, how they attempt to deal with the challenge of decoupling client from server, evolvability, extensibility, adaptability, composability.
The biggest challenge is that needs change over time, and APIs must necessarily evolve. Versioning is challenging, and breaking changes are inevitable. You'll leave this session with a highlevel understanding of these approach, their respective tradeoffs and ultimately how to align your API approach with your architectural and organizational goals.
Since ChatGPT rocketed the potential of generative AI into the collective consciousness there has been a race to add AI to everything. Every product owner has been salivating at the possibility of new AIPowered features. Every marketing department is chomping at the bit to add a “powered by AI” sticker to the website. For the average layperson playing with ChatGPT's conversational interface, it seems easy however integrating these tools securely, reliably, and in a costeffective manner requires much more than simply adding a chat interface. Moreover, getting consistent results from a chat interface is more than an art than a science. Ultimately, the chat interface is a nice gimmick to show off capabilities, but serious integration of these tools into most applications requires a more thoughtful approach.
This is not another “AI is Magic” cheerleading session, nor an overly critical analysis of the field. Instead, this session looks at a number of valid usecases for the tools and introduces architecture patterns for implementing these usecases. Throughout we will explore the tradeoffs of the patterns as well as the application of AI in each scenario. We'll explore usecases from simple, direct integrations to the more complex involving RAG and agentic systems.
Although this is an emerging field, the content is not theoretical. These are patterns that are being used in production both in Michael's practice as a handson software architect and beyond.
Architects must maintain their breadth, and this session will build on that to prepare you for the inevitable AIpowered project in your future.
Since ChatGPT rocketed the potential of generative AI into the collective consciousness there has been a race to add AI to everything. Every product owner has been salivating at the possibility of new AIPowered features. Every marketing department is chomping at the bit to add a “powered by AI” sticker to the website. For the average layperson playing with ChatGPT's conversational interface, it seems easy however integrating these tools securely, reliably, and in a costeffective manner requires much more than simply adding a chat interface. Moreover, getting consistent results from a chat interface is more than an art than a science. Ultimately, the chat interface is a nice gimmick to show off capabilities, but serious integration of these tools into most applications requires a more thoughtful approach.
This is not another “AI is Magic” cheerleading session, nor an overly critical analysis of the field. Instead, this session looks at a number of valid usecases for the tools and introduces architecture patterns for implementing these usecases. Throughout we will explore the tradeoffs of the patterns as well as the application of AI in each scenario. We'll explore usecases from simple, direct integrations to the more complex involving RAG and agentic systems.
Although this is an emerging field, the content is not theoretical. These are patterns that are being used in production both in Michael's practice as a handson software architect and beyond.
Architects must maintain their breadth, and this session will build on that to prepare you for the inevitable AIpowered project in your future.