# Autonoma: A Hierarchical Multi-Agent Framework for End-to-End Workflow Automation

Source: [Autonoma: A Hierarchical Multi-Agent Framework for End-to-End Workflow Automation](<https://www.sota2.com/research/paper/69c0c9010636e5bf4afc3b3f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-03-24T05:00:03.763Z

Authors: Eslam Reda, Maged Yasser, Sara El-Metwally

Published: 2026-02-27T11:00:37.000Z

## Abstract

The increasing complexity of user demands necessitates automation frameworks that can reliably translate open-ended instructions into robust, multi-step workflows. Current monolithic agent architectures often struggle with the challenges of scalability, error propagation, and maintaining focus across diverse tasks. This paper introduces Autonoma, a structured, hierarchical multi-agent framework designed for end-to-end workflow automation from natural language prompts. Autonoma employs a principled, multi-tiered architecture where a high-level Coordinator validates user intent, a Planner generates structured workflows, and a Supervisor dynamically manages the execution by orchestrating a suite of modular, specialized agents (e.g., for web browsing, coding, file management). This clear separation between orchestration logic and specialized execution ensures robustness through active monitoring and error handling, while enabling extensibility by allowing new capabilities to be integrated as plug-and-play agents without modifying the core engine. Implemented as a fully functional system operating within a secure LAN environment, Autonoma addresses critical data privacy and reliability concerns. The system is further engineered for inclusivity, accepting multi-modal input (text, voice, image, files) and supporting both English and Arabic. Autonoma achieved a 97% task completion rate and a 98% successful agent handoff rate, confirming its operational reliability and efficient collaboration.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Multi-agent assistant system evaluation on Autonoma System 500 cases (test)](<https://www.sota2.com/research/sota/multi-agent-assistant-system-evaluation-on-autonoma-system-500-cases-test.md>): Multi-agent assistant system evaluation; Autonoma System 500 cases (test); Task Completion Rate: 97.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.19270>)

[PDF](<https://arxiv.org/pdf/2603.19270.pdf>)