Nick Nisi

This talk explores how to improve AI agent performance by reducing complexity and focusing on verifiable outcomes. The speaker advocates for enforcing actions through structured pipelines rather than relying on agent instructions alone. By minimizing unnecessary agent skills and implementing robust verification mechanisms, developers can achieve better results and reduce errors. The core idea is to shift from trusting agents to demanding proof of their work.

This workshop explores the concept and practical application of "skills" in AI agents, presenting them as discrete, composable units of work designed to enhance agentic workflows. The core thesis is that skills provide a structured way to encode essential context, instructions, and behaviors, preventing repetitive information input and ensuring consistent, predictable agent performance across various tasks and projects.

This workshop introduces Mastra.ai, a framework for building AI applications in TypeScript. It demonstrates how to create agentic workflows, focusing on composable pipelines, tools, and agents. The session highlights using TypeScript for production-ready AI applications, emphasizing patterns applicable to deploying AI solutions.