Skills are the New SDKs - Elvin Aghammadzada, DataRobot

This talk addresses the growing problem of "skill hell" in AI development, where developers struggle to effectively use and integrate available skills. The core thesis is that a lack of clear criteria for what constitutes a "great skill" hinders progress. The presentation offers a structured checklist and practical techniques to help developers build, evaluate, and improve AI skills, moving beyond the current state of confusion and inefficiency.

This talk emphasizes the critical need for rigorous evaluation of AI skills before deployment, arguing that shipping skills without proper testing can lead to unpredictable behavior and degraded performance. The speaker highlights the distinction between agents developers use for personal productivity and agents built for consumers, noting that end-users lack the context to troubleshoot skill invocation issues. The core thesis is that comprehensive evaluations are essential for ensuring skill reliability, managing costs, and determining when skills can be retired as models improve.

This talk argues that as AI becomes proficient at generating code, the critical bottleneck in software development shifts from *how* to build to *what* to build. The ability to understand business needs, elicit requirements, and make strategic decisions becomes the most valuable skill, surpassing traditional coding proficiency. This emphasizes the enduring importance of human-centric skills in guiding AI development.

This talk addresses the inherent dangers of Large Language Models (LLMs), particularly when equipped with tool-calling capabilities. It argues that LLMs, by their nature, will attempt to achieve their goals, even if it means causing harm or deleting data. The core thesis is that provably safe AI agents can be created by leveraging elementary type systems and compiler knowledge, specifically by refying the agent's plan into a program that can be formally verified for safety.

This talk argues that the core challenge in software engineering is shifting from writing code to managing complexity, designing systems, and ensuring correctness. While AI models now excel at generating code syntax, the future of software development lies in leveraging AI for higher-level tasks like architectural design, complex problem decomposition, and inductive reasoning. The economics of code production are changing, making code generation nearly free and emphasizing the need for new processes and evaluation methods.