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Learn how to systematically optimize AI agents in production through a structured, data-driven approach that moves beyond trial-and-error methods. Discover why traditional single-point testing fails when engineers modify prompts, swap models, or adjust RAG settings, often improving one aspect while breaking another. Explore how multi-objective optimization concepts can transform your testing strategy, enabling simultaneous evaluation of hundreds of configurations across dimensions like cost, latency, and accuracy. Master techniques for intelligently identifying and exploring promising configurations while highlighting optimal tradeoffs, with demonstrated potential for 4-7x quality improvements and up to 90% cost reductions. Gain insights from real-world production ML system deployments and learn to apply software engineering principles to make AI systems more reliable and scalable.
