Closing the Continuous Learning Loop: From Agent Traces to Agent Training
Kurz zusammengefasst
ai Production traces show where agents fail, but how do we turn those failures into learning? This talk explores a practical loop from traces to reproducible simulations, verifiable rewards, and model improvement through fine-tuning or reinforcement learning.
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AI Performance Engineering Meetup (Munich2) Zoom link: https://us02web.zoom.us/j/82308186562
Talk #0: Introductions and Meetup Updates by Chris Fregly and Antje Barth
Talk #1: Closing the Continuous Learning Loop: From Agent Traces to Agent Training by Rustem Feyzkhanov, Engineering Manager @ Snorkel.ai Production traces show where agents fail, but how do we turn those failures into learning? This talk explores a practical loop from traces to reproducible simulations, verifiable rewards, and model improvement through fine-tuning or reinforcement learning. We’ll also look at whenit’s better to change prompts, tools, or the agent harness instead ofupdating model weights.
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links Github Repo: http://github.com/cfregly/ai-performance-engineering/ O'Reilly Book: https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/ YouTube: https://www.youtube.com/@AIPerformanceEngineering Generative AI Free Course on DeepLearning.ai: https://bit.ly/gllm
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