Field notes for AI-native operators
Essays on turning prompts, tools, and experiments into systems that can survive real work.
Brandon Galang
I help operators turn messy work into AI-native systems.
Product leader, hands-on agent builder, and author of Leverage Loops. I study how real workflows become repeatable systems through context, execution, review, feedback, and memory.
Current Work
Leverage Loops is the public lab.
Agent systems and workflow design
Practical builds around context delivery, review loops, local apps, automation, and product surfaces.
Operator-builder teaching
Playbooks and artifacts for people who know the work and want enough technical agency to shape the system.
Working Principles
The useful frontier is the loop around the model.
- Task shape before prompt quality. The first unlock is defining the work clearly enough for an agent to hold.
- Visibility at least equal to task requirements. Agents need enough surface area to inspect, act, and verify.
- Taste is undocumented criteria. Good systems expose judgment instead of pretending it is obvious.
- Speed only matters after trust. The loop has to make errors legible before acceleration helps.