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Open-source framework that runs AI agents as a coordinated team. Features Director-led orchestration, workspace memory, automatic handoffs, and conditional quality gates. Includes 62K-word implementation guide documenting real failures and solutions.

I built a multi-agent orchestration system and turned the dev exhaust (tests, Git commits, CLI docs) into a free ebook. It s not theory: it documents the architecture, failures, refactors and ops decisions that made it production-ready. 5 lessons that actually moved the needle 1. Architecture > prompts. The wins came from memory, quality gates, orchestration, and service layers not better prompts . 2. Hire teams dynamically. A Recruiter AI assembles the right agent team per goal/domain; hard-coding roles doesn t scale. 3. Unify orchestration. Consolidating multiple orchestrators into a Unified Orchestrator cut conflicts and latency, and improved completion rates. 4. Production readiness is a discipline. We built a Production Readiness Audit to stress security, scalability, and performance beyond it works on dev . 5. Load reveals truth. A load-testing shock forced pragmatic quality thresholds and better prioritization systems get smarter under stress. Questions for the community How are you deciding when to use structured vs adaptive orchestration at runtime? What s your bar for quality gates so you don t stall progress? Would you find more useful: a starter repo + checklists, or deeper chapters on monitoring/telemetry & cost control? Link (free beta): books.danielepelleri.com P.S. The ebook was compiled automatically from the project s tests, commits, and CLI-generated docs so the narrative mirrors the real workflow, not a cleaned-up case study.

TL;DR I built a multi-agent AI orchestration system and let AI generate an ebook from real tests, Git commits, and CLI-generated docs. Free, practical, and very in the trenches . What it is AI Team Orchestrator an AI-generated captain s log of the build: architecture, failures, fixes, and lessons learned. Why it s different Not theory. It s compiled from the actual dev exhaust (tests/commits/docs), so the narrative mirrors the real workflow. Who it s for Builders shipping with agents, founders validating AI ops, and devs curious about orchestration beyond toy demos. Link books.danielepelleri.com Ask (feedback welcome!) 1. Which chapter needs the most depth (architecture, evals, guardrails, ops)? 2. Would a starter repo + checklists be more useful than more chapters? 3. What s missing to apply this in a real startup stack? Self-promo, yes but I m genuinely looking for critique and use-cases. Happy to share the raw prompts/pipeline if helpful.