Production-grade agent coordination framework for autonomous development and zero token bloat.
Standard AI coding assistants suffer from hallucinations, lost instructions over long conversations, and massive token costs when working on complex multi-step codebases.
Single-agent LLMs attempt to plan, code, test, and review simultaneously, leading to sloppy regressions, skipped unit tests, and conversational context exhaustion.
AI engineers, autonomous agent developers, and teams building production LLM developer tools.
A structured persona and skill hierarchy where an Orchestrator deconstructs goals into atomic tasks, an Engineer writes test-first code, and a Code Reviewer enforces strict quality gates before completion.
System Architect & Author — Created the skill and persona specifications, authored the execution contracts, and integrated Headroom cache compression.
Enforces strict test-driven development: write failing test, write code to pass, commit atomically.
Dedicated code-reviewer and test-engineer personas block merges until all lint and coverage criteria pass.
Compresses verbose subagent execution transcripts while preserving full semantic retrieval capability.
Designed skills as human-readable Markdown files so developers can inspect and refine agent instructions using standard git version control.
Isolated persona responsibilities to eliminate self-review bias in autonomous code creation.
Preventing agent looping required strict state transition timeouts and unambiguous exit criteria in each skill definition.
In-browser chess academy featuring 6 calibrated bots, live mistake explanation, blunder safety nets, and offline PWA support.
End-to-end video creation pipeline coordinating 7 specialized AI agents to take a topic from raw concept to scripted, voiced production plan.
Frictionless customer review collection SaaS using LLMs to draft personalized reviews while complying strictly with FTC & Google policies.