---
purpose: Ground lessons about why agentic software factories fail.
applies_to: Teaching sessions about safe AI-assisted software development.
entrypoint: Start with lessons/0001-why-software-factories-fail.html.
verification: Learner can diagnose reward, blind spot, delay, and steering point.
update_when: The learner's desired real-world outcome changes.
---

# Mission: Understand Why Software Factories Fail

## Why

Develop sound judgment about agentic software workflows so increased coding throughput does not quietly destroy maintainability. Use that judgment to recognize unsafe “lights-off” automation and choose where human steering has the highest leverage.

## Success looks like

- Explain the causal mechanism behind software-factory failure without relying on slogans
- Distinguish fast verification from evidence of long-term maintainability
- Diagnose whether an agent workflow is optimizing the wrong feedback signal
- Choose an appropriate human review point for a real change

## Constraints

- Lessons should be short, concrete, and applicable to real repositories
- Prefer primary sources and observable evidence over AI-industry hype
- Build durable recall through retrieval practice

## Out of scope

- Building a fully autonomous software factory
- Comparing specific coding-agent products
