The Factory Model for AI Agents: WIP Limits, Flow, and 10x Throughput
Workshop
AI agents are becoming part of the software development process, but most teams treat them like isolated tools rather than participants in a structured workflow. Without coordination, agent-driven development quickly turns into chaos: duplicated work, endless retries, and unpredictable delivery.
I will introduce a practical approach to organizing AI agents using principles from lean manufacturing and Kanban flow systems. By applying concepts such as pull-based work, WIP limits, and bottleneck management, engineering teams can orchestrate multiple AI agents—system analyst, developer, and tester—into a predictable software delivery pipeline.
Through a live demonstration using GitHub Projects and modern AI coding tools, I will show how agents autonomously pull tasks, move work across pipeline stages, and escalate to humans only when necessary. The result is a development workflow that reduces coordination overhead while dramatically improving throughput and visibility.
I will introduce a practical approach to organizing AI agents using principles from lean manufacturing and Kanban flow systems. By applying concepts such as pull-based work, WIP limits, and bottleneck management, engineering teams can orchestrate multiple AI agents—system analyst, developer, and tester—into a predictable software delivery pipeline.
Through a live demonstration using GitHub Projects and modern AI coding tools, I will show how agents autonomously pull tasks, move work across pipeline stages, and escalate to humans only when necessary. The result is a development workflow that reduces coordination overhead while dramatically improving throughput and visibility.