Delete Waste

Delete waste deliberately. Every agent, command, hook, and constraint in Gemba Flow exists to close one of the seven wastes from Toyota’s lean manufacturing system, translated for software delivery.

The seven wastes

1. Inventory → Partially done work

Unfinished code, features, or documentation sitting idle. An unsupervised agent can churn out half-finished PRs faster than humans can review them. Without constraints, you accumulate inventory at machine speed.

Countermeasures: Ready-column cap of 2-5 items; one ticket at a time; worker → reviewer → human pipeline; short-lived feature branches that force completion.

2. Overproduction → Extra features

Building functionality nobody asked for. Agents are eager to please: ask one for a login page and you get OAuth, magic links, biometrics, and a password strength meter. Overproduction is the default mode for generative AI.

Countermeasures: Product Manager gates what gets built; Product Owner gates what is ready to build; /lock-scope formalizes the MVP boundary; acceptance criteria in tickets keep the agent honest.

3. Extra processing → Relearning

Every agent session starts with an empty context window. Without institutional memory, the agent re-reads the same files, re-discovers the same patterns, and makes the same mistakes — every single time. Relearning waste becomes catastrophic at agent speed.

Countermeasures: Memory MCP entities (CompletedTicket, PatternDiscovered, LessonLearned) persist across sessions; the 4 Power Sections in tickets front-load context; /log-session captures what each session learned.

4. Transportation → Handoffs

Agent-to-agent handoffs are fragile. Context windows do not transfer. If the worker’s implementation intent is not captured in the PR description, the reviewer agent reviews blind.

Countermeasures: Structured PR template; GO/NO-GO review format; ticket format with 4 Power Sections; ensure-github-account.sh hook for identity switching at handoff boundaries.

5. Waiting → Delays

Agents work fast but block on human decisions. A PR sitting unreviewed for two days has wasted the agent’s speed advantage entirely. The bottleneck shifts from code production to human review bandwidth.

Countermeasures: Ready column always stocked; CI auto-fix protocol (up to 3 retries); reviewer agent starts review immediately; the human’s role is scoped to the final merge decision — highest-value, lowest-frequency.

6. Motion → Task switching

Frequent context switching reduces focus. An agent that juggles three tickets will do all three poorly. Context windows are finite — switching tasks means losing context, which means relearning.

Countermeasures: One ticket at a time; agent role specialization (each agent does one type of work); /work-ticket does exactly one thing; explicit “no parallel work” rule.

7. Defects → Defects

Agents produce plausible-looking code that may be subtly wrong. They do not feel uncertainty — they generate with equal confidence whether the code is correct or hallucinated. Defect generation can outpace defect detection unless the system is designed to prevent it.

Countermeasures: Quality engineer agent writes BDD plans up front; PR reviewer red flags; pre-push hooks; CI gates; “never merge with failing tests” as a hard rule; error receiver auto-files production bugs as tickets.

Vibe coding optimizes for code generation throughput. Lean says throughput of code is irrelevant; what matters is throughput of value to the customer.