AI-assisted software factory

AI changes the system around coding

An assistant or agent can make a change arrive faster, but the factory still needs to understand, review, test, secure, deploy, and operate it. If downstream queues are already constrained, more generated output can increase unfinished work or rework instead of increasing useful delivery.

Measure AI use in context. Adoption and accepted suggestions are early signals. Pair them with change size, review demand, build and test feedback, code quality, rework, deployment stability, developer experience, cost, and product outcomes. Keep direct telemetry separate from estimates inferred from repository activity.

Treat the factory as the unit of improvement

Compare similar work before and after an AI workflow change. Record the tool, task type, model or agent path when relevant, human review policy, and time window. Look for bottlenecks that move downstream. A change that improves local coding speed but increases review or recovery work has not improved the whole factory.

How Weave can help

Weave provides a development-level view of how AI-associated work moves through code, review, quality, and delivery. It helps teams investigate the system effect while product, platform, cost, and operational data establish the full result.