More efficient agents - Learn from my mistakes

AI agents promise autonomy, reasoning, and efficiency—but in practice they often lose context, make questionable decisions, or behave like they’ve forgotten what they were doing halfway through a task.
If that sounds familiar, you’re not alone.
In this session, I’ll share the real-world mistakes I made while building and experimenting with AI agents—and the practical solutions that actually worked. We’ll look at how to improve reasoning, preserve context, design better decision flows, and avoid common architectural pitfalls that quietly kill performance.
With more vendors shifting from “hook you in” pricing to transparent pay‑for‑what‑you‑use models, efficiency isn’t just a technical concern anymore—it’s a financial one. I’ll show you how smarter agent design can significantly reduce costs without sacrificing capabilities.
You won’t leave with a “perfect” agent—but you will leave with proven patterns, hard-earned lessons, and clear ideas you can apply immediately to build more reliable, predictable, and cost‑efficient AI agents.

Writing better skills and rules

Your AI agent isn’t misbehaving—it’s doing exactly what you told it to do.
Even the most capable models will fail if the skills, rules, and contextual boundaries around them are poorly written. As AI agents become more autonomous, these elements matter far more than clever prompting—and when done wrong, they don’t just cause hallucinations or erratic behavior, they quietly drive up costs.
In this session, we’ll focus on how small wording choices in skills and rules can have a massive impact on agent behavior. You’ll learn how to reduce hallucinations, prevent goal drift, control autonomy, and make agent decisions more predictable and repeatable.
We’ll also explore how unclear or overly permissive rules can lead to unnecessary reasoning loops, tool misuse, and inflated token usage—and how to fix that without restricting your agent’s usefulness.
This talk is about moving from “prompt tweaking” to intentional agent design, so your agents behave the way you expect—reliably, transparently, and cost‑efficiently.

Key takeaways:
* What “skills” and “rules” actually mean in modern AI agent architectures
* Common mistakes teams make when defining agent behavior
* Patterns for writing clear, robust, and composable skills
* Techniques for enforcing boundaries without over‑constraining the model
* How to test, iterate, and evolve rules as agents grow more autonomous

The only AI adoption strategy you'll ever need in any company

AI isn’t failing in companies because of technology—it’s failing because of how we adopt it. Despite massive investments, most organizations are stuck in pilots, fragmented initiatives, and unclear ROI. This session cuts through the hype and delivers a radically simple, battle-tested approach to AI adoption that works across industries, company sizes, and maturity levels.
In this talk, you’ll discover a practical, no-nonsense strategy that aligns AI with real business outcomes from day one. Instead of chasing tools, trends, or “innovation theater,” we focus on a repeatable framework that drives measurable impact: identifying high-value use cases, embedding AI into core workflows, and scaling adoption sustainably across teams.
Through real-world examples and lessons learned from both successes and failures, you’ll learn:

* Why most AI initiatives stall—and how to avoid the common traps
* How to prioritize use cases that deliver immediate ROI
* The critical role of leadership, culture, and governance in scaling AI
* A step-by-step model to move from experimentation to enterprise-wide impact
* How to make AI adoption stick—beyond hype cycles and pilot projects

Whether you’re an executive shaping strategy, a technology leader driving implementation, or a practitioner navigating change, this session will equip you with a clear, actionable blueprint you can apply immediately.
If you’re tired of endless AI discussions without results, this session will show you how to turn AI into a real competitive advantage—without overcomplication.
One strategy. Any company. Real results.

Most AI initiatives fail—not because of the technology, but because of how organizations approach adoption. In this session, you’ll learn a simple, practical strategy to turn AI from scattered experiments into real business impact. Expect clear frameworks, real-world examples, and actionable steps you can apply immediately—no hype, no fluff, just what works.