Qualixar OS: A Universal Operating System for AI Agent Orchestration

Computer Science > Artificial Intelligence [Submitted on 7 Apr 2026] Qualixar OS: A Universal Operating System for AI Agent Orchestration Varun Pratap Bhardwaj We present Qualixar OS, the first application-layer operating system for universal AI agent orchestration. Unlike kernel-level approaches (AIOS) or single-framework tools (AutoGen, CrewAI), Qualixar OS provides a complete runtime for heterogeneous multi-agent systems spanning 10 LLM providers, 8+ agent frameworks, and 7 transports. We contribute: (1) execution semantics for 12 multi-agent topologies including grid, forest, mesh, and maker patterns; (2) Forge, an LLM-driven team design engine with historical strategy memory; (3) three-layer model routing combining Q-learning, five strategies, and Bayesian POMDP with dynamic multi-provider discovery; (4) a consensus-based judge pipeline with Goodhart detection, JSD drift monitoring, and alignment trilemma navigation; (5) four-layer content attribution with HMAC signing and steganographic watermarks; (6) universal compatibility via the Claw Bridge supporting MCP and A2A protocols with a 25-command Universal Command Protocol; (7) a 24-tab production dashboard with visual workflow builder and skill marketplace. Qualixar OS is validated by 2,821 test cases across 217 event types and 8 quality modules. On a custom 20-task evaluation suite, the system achieves 100% accuracy at a mean cost of $0.000039 per task. Source-available under the Elastic License 2.0. Comments: 20 pages, 7 figures, 8 tables. Zenodo DOI: https://doi.org/10.5281/zenodo.19454219 Subjects: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Software Engineering (cs.SE) Cite as: arXiv:2604.06392 [cs.AI]   (or arXiv:2604.06392v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.06392 Focus to learn more Related DOI: https://doi.org/10.5281/zenodo.19454219 https://doi.org/10.5281/zenodo.19454219 https://doi.org/10.5281/zenodo.19454219 https://doi.org/10.5281/zenodo.19454219 Focus to learn more Submission history From: Varun Pratap Bhardwaj [view email] [v1] Tue, 7 Apr 2026 19:22:20 UTC (31 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.MA cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)