TITLE:
CUTEADMOA: A Unified Mixture-of-Agents Platform with Continuous Self-Training for Autonomous Enterprise AI Automation
AUTHORS:
Md Abu Sayeed
KEYWORDS:
Mixture-of-Agents, Multi-Agent Orchestration, Autonomous AI, Continuous Learning, Model Routing, Enterprise Automation, Retrieval-Augmented Generation, Self-Improving Systems
JOURNAL NAME:
Advances in Artificial Intelligence and Robotics Research,
Vol.2 No.3,
September
24,
2026
ABSTRACT: The rapid proliferation of large language models has produced powerful but fragmented automation tooling: mixture-of-agents frameworks that improve output quality, routing systems that reduce inference cost, orchestration frameworks that coordinate teams of agents, and lifelong-learning methods that enable models to improve over time. Each advance is typically delivered as an isolated component with no integrated path from interaction capture to model improvement. This paper presents CUTEADMOA, a unified production platform that integrates heterogeneous model aggregation, intent-based smart routing, multi-tier reliability engineering, enterprise security scanning, AI-powered web automation, and multimedia generation within a single zero-dependency architecture. It is accompanied by CUTTYMOA-1.0, a continuously self-trained companion model driven by a closed-loop pipeline that harvests production interactions, curates them into 204 verified datasets spanning 24 categories, fine-tunes a mixture-of-experts base model, updated model into the serving path, with controlled evaluation of retrained-model improvement planned as future work (Sections 6.3 and 7). The system captures every conversation, application programming interface call, and security scan into a streaming knowledge corpus exceeding 857,000 training pairs with a large keyword index for retrieval-augmented generation. We describe the architectural design, the continuous learning pipeline, and the operational engineering, including redundant gateway proxies with independent credential-pool rotation, preemptive rate-limit handling, and a failover chain of more than twenty fallback slots, that distinguishes the platform from research prototypes. Operational evaluation shows deterministic latency tiers, sustained availability under credential exhaustion, and monotonic knowledge growth. We discuss limitations and outline future work on formal benchmarking and multi-tenant deployment. The principal contribution is a reference architecture for autonomous, self-improving AI platforms that closes the loop between usage and capability.