I’m John Young.
I run engineering at Combine Capital and Infrared, and write research-backed essays on running AI coding agents in production — every claim traced to a primary source.
github.com/johnayoung linkedin.com/in/jyoung1985 john.anto.young@gmail.com rss
Ledger
Essays12
Pillars6
Evidence base verified2026-07-01
Recent Essays
- Loop Engineering Breaks Your Single-Shot Context Playbook Context Engineering A loop was always the agent primitive. The CLAUDE.md budget and JIT retrieval you tuned for one invocation don’t fail louder in a loop — they fail quieter.
- You Can’t Cap What You Can’t Attribute: Per-Task Cost Production Operations A cost dashboard is a lagging report, not a guardrail. Per-task cost attribution is the schema that makes an agent budget ceiling enforceable before the next call.
- Where Just-in-Time Context Retrieval Silently Breaks Context Engineering JIT context retrieval isn’t free — it’s slower and only as good as your tooling. A ledger of where runtime retrieval breaks and how to design the fallback.
- When One Agent Stops Being Enough: The Isolation Gate Architecture Decisions The trigger for going multi-agent isn’t parallel speed — it’s context isolation, and the split costs about 15x the tokens. Two gates decide whether it’s worth it.
- Review Capacity Is the Real Ceiling on Your Agents Team & Process The ceiling on how many AI coding agents your team can run is reviewer-hours, not agent output — and that number is already in your telemetry. Here’s how to size it.