Michael Carducci

Gartner just named the semantic layer a non-negotiable foundation for AI. Almost nobody in the industry knows what that means yet, or why it should worry them.

Every LLM response is a guess, not a fact. Most of the time it's close enough that nobody notices. Then it's wrong, and there was no way to see it coming, because the model doesn't know what your data means—the model doesn't know anything. It's always guessing, based on patterns, every time. The guess is right often enough to feel like magic. It will never be right 100% of the time, and can't change that. It's not a bug waiting on a fix. It's a hole in your architecture.

As one recent paper on the semantic gap puts it:

> “When AI systems enter the picture, descriptive documentation is insufficient. LLM-based interfaces operate directly over data representation. If meaning is not encoded structurally, the model infers it probabilistically.”

Your systems are full of structured data (JSON, databases, APIs) that means something to the code that built it and nothing to anything else, including the AI you just pointed at it. A field called status: 3 means nothing outside your system. Multiply that by every field, every service, every team, and you get an enterprise fluent in nothing but itself. That's the wall every serious AI initiative eventually hits, and it's why so many AI initiatives are underdelivering.

The fix isn't a bigger model or a new platform. It's a set of open standards that have quietly run major parts of the web for decades, built for making data mean something on its own, without a person or a program standing by to translate it. Once your data can say what it is, your AI stops guessing about it. Data-driven interactions become deterministic-first, your entire enterprise data landscape becomes more powerful, your token efficiency goes through the roof, and whole classes of problems in AI disappear. That's the semantic layer.

This workshop is a hands-on introduction to building one, starting from data you already have, with tools you already know. You'll leave with a working model of what a semantic layer actually is, why it's about to become as fundamental as the database, and why the people who understand it now will be the most valuable engineers in their organization for the next decade, while everyone else is still tweaking prompts.

In tech teams it's a constant firefight. We react. Then we react to the reaction… the cycle continues. In all this noise, in all this chaos, how do we move forward. How do we remain proactive?

A great leader must be an enabler for the team. At times this means insulating the team from the noise. At other times it means improving the environment for the team. At all times, however, it requires setting clear priorities and conditions for success.

This session is focused on the art of moving forward in even the noisiest environments.

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.

AI is accelerating software development at an unprecedented pace, but many teams are discovering a frustrating reality: faster coding isn’t translating into faster delivery.

The reason is counterintuitive. When you accelerate one part of a system, you don’t improve the system… you stress it. More code becomes more review, more coordination, more cognitive load, and ultimately, less flow.

This talk connects that modern failure mode to a foundational systems insight from The Goal: local optimization usually degrades overall performance. From there, Michael Carducci shows how to apply the Theory of Constraints to modern software delivery.

Using concrete examples, you’ll see how practices like XP, DevOps, Domain-Driven Design, and Team Topologies act as targeted interventions on specific bottlenecks—and how misapplying them can make things worse.

You’ll leave with a practical mental model for identifying constraints in your system, reasoning about trade-offs, and designing for flow in an AI-accelerated world.