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ORGANIZER:MAILTO:avner.algom@gmail.com
TITLE:From Guesswork to Greatness: Systematic AI Agent Optimization in Production
DTSTART:20250904T111500Z
DTEND:20250904T113000Z
SUMMARY:From Guesswork to Greatness: Systematic AI Agent Optimization in Production
DESCRIPTION:Every engineer building AI agents has faced it: you tweak a prompt, swap a model, or change a RAG setting—only to realize it either made things worse or improved one thing while breaking another. Why does this happen? Because teams are testing just one version of the agent out of the million possible combinations—and hoping it works.Today's evaluation tools are built for single-point assessment, not the kind of massive multi-dimensional comparison you actually need—sure, they might let you A/B test two prompts or pick between three models, but they can't help you explore hundreds of combinations across cost, latency, and accuracy all at once.In this talk, we’ll show how a structured approach to testing alternatives can flip the script. Borrowing ideas from multi-objective optimization, we’ll show how our SDK and UI let engineers allocate their testing budget while Traigent intelligently explores high-potential configurations and surfaces the best tradeoffs. You’ll see how this approach leads to 4–7x quality gains and up to 90% cost savings—without blind guesses or manual trial-and-error.
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