The AI-Native
Operations Manual.
The orchestrator + sub-agent stack we use to compress weeks of work into hours.
The AI-Native Operations Manual
Most teams 'using AI' are pasting into ChatGPT. This manual is the operating layer above that — orchestrator + sub-agent architecture, tool registry, eval harness, deployment + observability. The same system we run internally to do a senior team's week of work in an afternoon.
The system you've been trying to build.
This isn't a course. It's the working artifact we hand new team members on day one. Operator-grade, version-controlled, pressure-tested at scale.
Orchestrator → sub-agent pattern
Why a single LLM call is the wrong unit of work, and how to decompose tasks across a planner + specialist fleet with proper isolation.
Cost controls that actually work
Rate limits, token budgets, circuit breakers, and the exact middleware we use to keep a $10K/mo agentic system from becoming a $50K/mo agentic system.
Evals that catch regressions
The harness, the rubric, and the workflow for getting confident that today's prompt edit didn't break yesterday's tasks.
Concrete outcomes. Not promises.
- An orchestrator that fans out work to specialist sub-agents
- Cost controls that prevent runaway spend
- An eval harness so you don't ship regressions
- A failure-mode taxonomy with proven mitigations
- Production observability you can actually debug from
- Operators rolling out agentic systems internally
- Engineering leaders building AI features
- Founders tired of pasting into ChatGPT
- Teams looking for a no-code automation guide — different doc
- Companies without a technical lead to wire it up
- AI tourists looking for hype, not architecture
Reviews (141)
The orchestrator pattern alone is worth 20x what I paid. We've been winging this for a year.
Finally a guide that treats agents like software, not magic.
The eval harness section is the missing piece nobody else writes about.
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