Show HN: Multi-agent AI orchestration – lessons from a build log I built a multi-agent AI orchestration system and then had AI generate the ebook about the build from the project’s real artifacts: test outputs, Git commits, and CLI-generated docs.
It’s not a cleaned-up case study; it mirrors the actual workflow (failures, refactors, trade-offs). What it is • AI Team Orchestrator: a free beta “captain’s log” of the build • Focus: architecture, orchestration patterns, memory, quality gates, evals/guardrails, and ops Why it’s different • Content was compiled automatically by AI from dev exhaust (tests/commits/docs), then lightly edited • Emphasis on applied practices over prompt tinkering Who it’s for • Builders shipping multi-agent systems • Founders/PMs evaluating AI ops beyond toy demos • Engineers interested in the glue: orchestration > single-agent prompts Looking for feedback 1. Where should I go deeper: orchestration vs. monitoring/telemetry vs. cost control? 2. Would a starter repo + checklists be more useful than more chapters? 3. What quality-gate thresholds do you use to keep progress moving without rubber-stamping junk? Notes • Free, no email gate • Early beta; happy to share prompts/pipeline if useful |