Lift 3: Systems Over Supervision¶
Where We're Starting¶
Your evals tell you what's wrong. Your structured logging tells you where it went wrong. Your feedback loop tells you how to fix it. But fixing it — and managing the flow — is still all on you. Every eval failure lands on your desk. Every trace investigation is your time. Every skill refinement is your judgment.
You've automated the measurement, but the response to measurement is still manual. You're a bottleneck again, just at a higher level.
This lift addresses that by shifting your role from doing the work to designing the systems that do the work. You define the structure, set the quality bar, and build the guardrails. Agents fill in the rest.
What You'll Learn¶
- How eval failures become auto-created work items that agents can pick up — turning measurement into action without you as the middleman
- How worktrees enable true parallel development — independent workstreams with isolated branches and separate conversations
- How quality gates enforce your standards at AI speed — and why evals belong in the deployment pipeline alongside tests and linting
- How to design a safety net that scales — build the guardrails, not every feature
Sections¶
- From Eval Failures to Work Items — Auto-creating issues, task graphs, and the system that fixes itself
- Parallel Development — Worktrees for independent workstreams — define the structure, don't fill it in
- Owning the Quality Bar — Quality gates, deployment guards, and building the safety net at scale
By the End of This Lift¶
- You can describe how eval failures become auto-created work items in a pipeline
- You understand how worktrees enable parallel development with isolated branches per workstream
- You can design a quality gate pipeline that blocks deployment when evals regress
- You know the difference between building features and building guardrails — and why the guardrails matter more at this level
- You can articulate the wall: automation is powerful, but where should humans still be in the loop?