Running AI coding agents can get expensive and unreliable. Agents burn tokens and sometimes invent data. This playbook addresses both problems. It lays out an 11-step master flow for a live multi-agent setup. It includes a one-page subagent preamble. The measured token use was about 99K tokens with the preamble, versus 200-310K without it. That is roughly a 55% cut.
The guide also covers a 3-tier model routing table and a 15-lever cost tree. It explains the adversarial-evaluator pattern with three anonymized real catches. It includes the atomic-lock pattern for safe parallel agents and the opportunity-engine pattern for agents that find safe next work on their own. The material is production-tested and written in English. It is delivered as a PDF from the publisher's store. The publisher is CESCAC Productos y Servicios S.A.S. in Guayaquil, Ecuador, operating since 2024.
What is inside
Taken from the product sheet on our store page, unchanged:
A production-tested operating system for running AI coding agents (Claude Code and similar) without burning tokens or letting agents invent data. Inside: the 11-step master flow, the one-page subagent preamble (measured ~99K tokens vs 200-310K without it, a ~55% cut), the 3-tier model routing table, the 15-lever cost tree, the adversarial-evaluator pattern with 3 anonymized real catches, the atomic-lock pattern for safe parallel agents, and the opportunity-engine pattern for agents that find safe next work on their own. PDF, delivered instantly, English.
Who it is for
- Developers running Claude Code or similar AI coding agents
- Engineering leads who want to control token spend
- Teams that need agents to stop inventing data
- Teams running multiple agents in parallel and needing safe coordination
- People who want agents to find useful next work on their own
How you actually use it
- Follow the 11-step master flow to structure a live multi-agent setup
- Use the one-page subagent preamble before delegating work to agents
- Refer to the 3-tier model routing table when choosing which model handles a task
- Apply the 15-lever cost tree to identify where tokens are being wasted
- Use the adversarial-evaluator, atomic-lock, and opportunity-engine patterns for review, parallel safety, and self-directed work
What it is not
This is a core system guide, not a full software tool. It does not run agents for you. The token figures describe one measured setup and may differ in your own environment. The guide covers the patterns listed in its description, not every possible agent workflow. It is a PDF reference for people already working with AI coding agents, not a beginner tutorial or a complete replacement for hands-on testing.
Questions we get
What does the guide include?
It includes the 11-step master flow, the one-page subagent preamble, the 3-tier model routing table, the 15-lever cost tree, the adversarial-evaluator pattern with three anonymized real catches, the atomic-lock pattern, and the opportunity-engine pattern.
What token savings does it show?
The measured preamble used about 99K tokens versus 200-310K without it, which is roughly a 55% cut. That is a measured result from the publisher's setup, not a promise for every environment.
How is the product delivered?
It is a PDF delivered instantly from the publisher's store page after payment. It is in English and costs USD 19.00 as a one-time payment.
Bottom line
This playbook is a practical reference for teams already running AI coding agents. It gives a structured flow, routing tables, and patterns for parallel work and self-directed agents. The measured token reduction is specific to one setup. The guide is available as an instant PDF download from CESCAC Productos y Servicios S.A.S. in Guayaquil, Ecuador.
CESCAC Productos y Servicios S.A.S. · RUC 0993389404001 · Los Ríos #609, Guayaquil, Ecuador · Digital store · Contact