A Federated Autonomous Agent Operating System for Infrastructure Operations

We present DRACONEX, a Federated Autonomous Agent Operating System for infrastructure operations that collapses six historically-separate operational disciplines — DevSecOps, IT Operations, Security Operations, Governance Risk and Compliance (GRC), Site Reliability Engineering, and IT Service Management — into a single coordinated autonomous platform. DRACONEX converts natural-language operator intent into validated, policy-gated, compliance-evidenced infrastructure actions across hybrid cloud, on-premises (on-prem), and air-gapped environments. The system architecture comprises a strategic Captain layer powered by DRACONEX-70B-v1, a 70-billion-parameter dense transformer fine-tuned on the proprietary DracoForce v7 training corpus and served at FP8 precision on an NVIDIA RTX Pro 6000 Blackwell GPU at 50 tokens/second sustained, and ten domain-specialized 8-billion-parameter agent brains (DRACONIDs) running DRACONEX-8B-v1, each operating as an independent inference process with its own tool registry, permission matrix, and autonomy loop. Unlike Mixture-of-Agents (MoAs) systems (Wang et al., 2024) that share a single underlying model across virtual agents and require continuous network connectivity to a shared inference endpoint, DRACONIDs are first-class processes with persistent state, independent failure domains, and the ability to continue operations when disconnected from the strategic brain. Unlike federated learning systems for distributed sensing and autonomous driving (Xiang et al., 2025), DRACONEX federates inference and action — not just gradient updates — across a hierarchical mesh of cloud, edge, and air-gapped nodes. The integration pattern is, to our knowledge, novel as a complete architecture. We claim that the result is more than an improvement on DevSecOps tooling — it is a new product category we call autonomous infrastructure operations, distinguished by the property of compliance-as-execution: ATO-grade audit evidence is produced as a primary output of normal operation rather than as a separate artifact-collection phase. The discipline named DevSecOps exists because humans had to manually integrate Development, Security, and Operations across organizational boundaries that nobody designed but everyone inherited; once an autonomous agent OS spans Development, Security, Operations, ITSM, GRC, and SRE simultaneously, the discipline name becomes archaeology. We describe the architecture, the training methodology behind the 92,877-seed v7 corpus (incorporating 206 real DISA STIGs and the full NIST 800-53 Rev. 5 control catalog), the federated intelligence mesh and its five knowledge-flow directions, the safety architecture (the only agent class with infrastructure write access is gated behind a three-party CAGE-FLY-Operator handshake), and benchmark results from production deployment.