Cumulative Flow Diagrams give your team instant X-ray vision into how work is being started and completed. By making WIP and cycle-time issues clearly visible, CFDs create real predictability and forecasting without “percent done” guessing. This visibility helps smooth out lumpy delivery, drives reduced batch size, and highlights the flow of code delivery. The result is a shared picture of reality that leads to better conversations and better decisions.

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The goal of this session is to show how to use AI to build real software without losing your mental model of the system.

This is a live demo starting from a problem domain chosen on the spot.

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Traditional Agile often prioritizes work based on the constraints of development teams and resources. AI automation makes some work trivial, and this session shows one practical way to sort backlog items into AI-ready and non-AI-ready categories based on many factors, such as risk, reward, prior disasters of similar types of work, etc.

The workflow demonstrated shows a new way of thinking about backlog prioritization in an AI world, and what I've learned over the past six months of applying these principles.

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