International Journal of Science, Engineering and TechnologyAuthors: Gurpreet Kaur, Savita, Nishu, Pooja, Aradhay Gupta
Abstract: We propose Ohmega, a composable AI framework that enables developers and end users to assemble pre-built miniature AI modules (micro-agents) via a drag-and-drop interface to construct larger, task-specific systems. These miniature AIs are small, focused components that each implement a single capability, such as text classification, entity extraction, arithmetic computation, API invocation, or simple policy decision-making. By treating such modules as first-class, reusable building blocks, Ohmega allows users to configure and reconfigure workflows without retraining large, monolithic models. The framework supports using modules in isolation or composing them in parallel or in sequence, with optional arbitration and ensemble strategies to aggregate or select outputs when multiple modules contribute to a decision. A central orchestrator manages data flow, enforces composition rules, and records execution traces, promoting transparency, debuggability, and safer behaviour. This design is intended to reduce the time, cost, and specialised expertise required to build reliable AI applications, while also making system behaviour more interpretable through explicit, inspectable graphs of interconnected micro-agents. Ohmega is particularly suited to multimodal AI, multi-agent systems, and LLM-centric applications in no-code or low-code settings, where domain experts may wish to prototype, adapt, and govern AI pipelines directly. By combining modularity, visual composition, and principled orchestration, the framework aims to accelerate prototyping and deployment of practical AI systems, encourage reuse of well-tested components, and provide a foundation for future work on automated composition, safety guarantees, and marketplace ecosystems for miniature AIs.
DOI: http://doi.org/10.5281/zenodo.21702525
