Agentic AI Flywheels
The article discusses the lifecycle of agentic AI systems, emphasizing the importance of a feedback loop after initial deployment. It outlines the pre-production phase and the recurring improvement cycle known as the Agentic AI Flywheel. The author also highlights an upcoming workshop aimed at teaching AI engineers how to effectively manage and improve their systems using evaluations.
- ▪Agentic systems typically start with a small evaluation set that grows based on production failures and user feedback.
- ▪The lifecycle of an agentic system consists of a pre-production phase followed by a recurring loop of shipping, observing, diagnosing, and improving.
- ▪The pre-production phase includes defining the problem, creating a proof of concept, establishing performance metrics, and developing a prototype with an initial evaluation set.
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Agentic AI FlywheelsThe production loop after your agent ships, and the eval set that grows with it.Aurimas GriciūnasMay 27, 2026151Share👋 I am Aurimas. I write the SwirlAI Newsletter with the goal of presenting complicated Data related concepts in a simple and easy-to-digest way. My mission is to help You UpSkill and keep You updated on the latest news in AI Engineering, Data Engineering, Machine Learning and overall Data space.SwirlAI Newsletter is a reader-supported publication. To receive new posts and support my work, consider becoming a subscriber.SubscribeMost agentic systems ship with a small initial eval set, accumulate production failures the eval set does not catch, and end up getting debugged from forwarded user complaints.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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