Sylwia Laskowska

AI agents seem complicated. But once you look under the hood, they're built from a few simple ideas.

In this talk, I'll use a real AI agent to show how its core loop fits in around 80 lines of code. We'll then explore the key ideas behind modern agents, including tool calling, conversation history, and stopping conditions.

I'll also explain what frameworks actually do and which problems they're designed to solve. You'll leave with a clear mental model of how AI agents work and the confidence to choose the right approach for your own projects.

Table of contents:
- AI agents are simpler than you think: an 80-line implementation
- What do we actually send to the LLM?
- How does the LLM know what to do next?
- How does tool calling work?
- Conversation history
- Stopping conditions
- Where frameworks really help: retries, fallbacks, security, and conversation management

Sooner or later, every team faces the same question: how do we migrate from legacy code to a modern framework? Should we take the “big bang” approach and rewrite everything from scratch, or should we migrate progressively, piece by piece? In this talk, I’ll share real-world stories from large-scale frontend migrations and highlight the trade-offs of both strategies. We’ll explore the risks of freezing development for months, the complexity of running two worlds in parallel, and the decision-making criteria that help teams pick the right path.

Sooner or later, every team faces the same question: how do we migrate from legacy code to a modern framework? Should we take the “big bang” approach and rewrite everything from scratch, or should we migrate progressively, piece by piece? In this talk, I’ll share real-world stories from large-scale frontend migrations and highlight the trade-offs of both strategies. We’ll explore the risks of freezing development for months, the complexity of running two worlds in parallel, and the decision-making criteria that help teams pick the right path