Enhancing Quality and Security in CI: Gunjan Patel

This talk focuses on mastering AI evaluation, moving from initial development to production. It emphasizes that even the best large language models (LLMs) require a robust testing framework due to issues like hallucinations and performance degradation with changes. Effective evaluation helps answer critical questions about model selection, cost-effectiveness, brand consistency, and ongoing improvement, ultimately reducing development time, costs, and enabling faster iteration.

The State of AI Engineering 2026 survey highlights evaluation (evals) as the most significant challenge across AI stacks. This talk delves into the survey results, emphasizing the critical need for robust evaluation methodologies in AI development. Addressing evals is presented as paramount for building reliable and high-quality AI systems.

The core thesis of this talk is that 2025 will finally be the year for robust AI evaluation, driven by the convergence of several key factors. The increasing understanding of AI by business leaders, coupled with budget shifts towards generative AI projects, has set the stage. Crucially, the rise of agentic systems, which make decisions and take actions, introduces significant complexity and risk, making rigorous evaluation a necessity rather than an option.

This talk emphasizes that robust evaluations are the most critical, yet often overlooked, component for successfully scaling generative AI workloads. The speaker argues that evaluations are not merely for measuring quality but are primarily a tool for discovering problems within AI systems. Implementing a strong evaluation framework is presented as the key differentiator between a project that remains a science experiment and one that achieves production-level success and scalability.

This talk introduces a framework for evaluating AI agents, dividing agent evaluation into semantic and behavioral aspects. Semantic evaluation focuses on how an agent's internal representations align with reality, while behavioral evaluation assesses how an agent's actions and tool usage contribute to achieving its goals. Both aspects are further categorized into single-turn and multi-turn scenarios, providing a comprehensive map for understanding and measuring agent performance.