Every Job makes an AI Agent smarter. Today we’re presenting AGI ALPHA ENGINE-003: Networked Skill Compounding Engine. The principle is simple: An AGI Job should not disappear after execution. It should become reusable learning. Every job produces one of three things: ✅ a validated Skill Package ✅ a rejected Skill Candidate ✅ a Failure Learning Package Accepted skills go into the Network Skill Vault. Other agents import them through Agent Skill Manifests. Then we test whether those agents actually improve on held-out tasks against a no-shared-skill baseline. That is the difference between agent automation and agent compounding. One Agent learns. All Agents can level up. Capabilities do not merely add up. They compound. But AGI ALPHA keeps the boundary strict: No fake metrics. No hard-coded wins. No autonomous promotion. No SOTA claim without Evidence Dockets. No persistence without human review. The claim gate is explicit: local bounded networked skill compounding is supported only when ProofBundles, Evidence Dockets, raw evaluator logs, replay, falsification, and B6-vs-B5 comparisons all pass. This is proof-bound machine labor. This is reusable agent capability. This is the beginning of a self-accelerating intelligence engine — governed, replayable, and evidence-first. GitHub: github.com/MontrealAI/agialp… Public site: montrealai.github.io/agialph… #AGIALPHA #AIAgents #RecursiveAI #AgenticAI #EnterpriseAI #MachineLabor #AISafety #ProofBundles #EvidenceDockets #SelfImprovingAI