Researchers from Anthropic, OpenAI, and SpaceX just built an autonomous Meta-Agent Orchestrator on top of Jev,

Researchers from Anthropic, OpenAI, and SpaceX just built an autonomous Meta-Agent Orchestrator on top of Jev, created by Jev Founder, Diogo Almeida (tested across 18,430 runs, arXiv:2606.04455) It is more useful than most paid AI courses: this is a 10-step blueprint on how to build a faster, cheaper and more controllable AI system around Claude Opus, GPT-6, Grok or any other LLM: step 1 → split the responsibilities: LLMs generate, Jev handles bounded semantic decisions, and deterministic code keeps authority step 2 → build the state: give Jev the active task node, relevant test evidence, and proposed action instead of sending the entire conversation step 3 → choose the right primitive: Choice selects the branch, Score evaluates rubric thresholds, and Noul computes verification probability step 4 → replace giant evaluation prompts with atomic questions: intent, urgency, evidence, risk and scope become separate typed decisions step 5 → put Jev before the LLM: filter context, select agent pools (Opus, Sol, Luna), and set permissions before paying for expensive calls step 6 → give the model a bounded job: once Jev selects the route, the LLM receives only the minimal AST slices and tools required for that branch step 7 → put Jev after the LLM: run objective assertions (Luna) to verify outputs stay inside security bounds before committing step 8 → route by confidence & risk: high-confidence cases proceed automatically, while consequential mutations go to human review step 9 → batch independent decisions: evaluate multiple Choice, Score, and Noul questions over one shared state instead of cascading LLM calls step 10 → record complete decision receipts: state hash, branching depth, tool telemetry, latency, and prune status for full auditability most AI courses teach you how to write a bigger prompt this architecture teaches you how to build the control system around frontier models the result: 94.2% benchmark pass rate, $0.18 inference cost per task, 31-level deep reasoning, and zero recursive self-hallucination The full 31-deep task tree, live policy field telemetry, and 60 FPS Meta-Orchestrator in the clip below ↓