Field notes on building with agents and humans
How to put agents to work without handing them the keys, how to design a pipeline that holds, and how teams ship faster while keeping a human on every merge. Written for the people doing the work.
What we write about
Supervising agents
Keeping AI fast and safe at the same time.
Pipeline craft
Designing stages and gates that earn their place.
The runtime for agents
Triggers, sandboxes, and validation in practice.
CI/CD for agents
What changes when agents open most of the pull requests.
Observability
Reading conclusions instead of noise.
Team practice
How planning, code, and operations come together.
Field notes, newest first.
Real release notes and practitioner write-ups only. No fabricated dates, no filler.
Building for scale: how JuhJuh handles multi-repo orchestration
Coordinating changes across multiple repositories is one of engineering's hardest problems. Here is how JuhJuh's worktree isolation model, service dependency mapping, and context flow make multi-repo development tractable.
Prompt engineering for backend developers: templates that scale
Writing effective prompts for backend development requires different patterns than frontend work. Here is how JuhJuh's template system turns prompt engineering from an individual skill into an organizational capability.
The cost of invisible AI: why token tracking matters
Your team already uses AI tools. The question is whether you know what it costs. Token-level attribution surfaces the true economics of AI-assisted development.
Why orchestration matters more than generation
Most AI coding tools optimize for the individual developer. That is the wrong unit of value. The real bottleneck is coordinating the pipeline from requirements to production across a team.
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