A software factory can build quickly and still fail if nobody decides what is worth building or retains enough understanding to support it after release. HumanLayer's lights-off experiment supplies the failure case: an outage forced the team back into code it had stopped reading. Repository preparation and Uber's shared context show what must exist before parallel agents can work safely. Continuous validation and a two-engineer case study then move review closer to intent and observable behavior. PostHog pushes the starting point further upstream, turning product errors and session replays into researched work rather than blind fixes. Stored lessons make the next feature cheaper, but one job stays human: judge the evidence, accept the risk and own what reaches production.
Why first: Horthy starts with the failure case. His lights-off factory produced working code faster than the team could preserve its understanding of the system, and an outage sent them back into code they had stopped reading. Zakariasson's readiness checklist is an answer to that mistake.
Why second: Horthy shows what unchecked output does to a codebase. Zakariasson works through the prerequisites: local patterns, automated tests, isolated environments and shared rules, with people keeping the architectural and security decisions that carry the most risk.
Why here: Zakariasson describes the pieces one team needs. Medisetty and Huda show them operating at company scale. Uber centralizes identity, privacy and tool access, then carries product research and designs through implementation, visual checks and a draft pull request.
Why here: Uber moves validation into the agent's inner loop but still stops at a draft pull request. Santos and Faulkner redesign the path for much higher throughput: begin with intent and a plan, validate in a stateful environment, reconcile competing changes, then ask a person to review the outcome.
Why here: Santos and Faulkner change the delivery pipeline. Spitz changes the organization around it. Specs become design documents, tickets and pull requests; agents take deterministic implementation and style checks, while people keep product feel, system design and security.
Why here: Spitz begins from an approved specification. Snyder starts earlier, with errors, messages and session replays coming out of the product. His system gathers evidence before it asks a coding agent to act, and sends an underspecified problem back to a person instead of inventing a fix.
Why here: Snyder wants later pull requests to learn from rejected fixes and production outcomes. Klaassen makes that habit the operating model. He spends as much effort storing the lesson from a shipped feature as he spends shipping it, so the next job begins with more of the team's judgment.
Why last: Klaassen describes a factory that remembers more after every feature. Osmani supplies its limit. More parallel agents create more routing, merging and verification decisions, while human attention stays finite. Someone must still judge the evidence, accept the risk and own what reaches production.