# Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration arXiv: https://arxiv.org/abs/2509.11067 ยท HTML: https://mlsanity.com/paper/2509.11067/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. - Builds on 28 of its 38 references that are in the map. - First posted on arXiv 14 Sep 2025. ## Placed in - LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/) ## Builds on (most cited first, 28 of 28) - [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024) - [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023) - [UI-TARS: Pioneering Automated GUI Interaction with Native Agents](https://mlsanity.com/paper/2501.12326/) (2025) - [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024) - [Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents](https://mlsanity.com/paper/2410.05243/) (2024) - [OS-ATLAS: A Foundation Action Model for Generalist GUI Agents](https://mlsanity.com/paper/2410.23218/) (2024) - [Aguvis: Unified Pure Vision Agents for Autonomous GUI Interaction](https://mlsanity.com/paper/2412.04454/) (2024) - [GUI-R1 : A Generalist R1-Style Vision-Language Action Model For GUI Agents](https://mlsanity.com/paper/2504.10458/) (2025) - [UI-TARS-2 Technical Report: Advancing GUI Agent with Multi-Turn Reinforcement Learning](https://mlsanity.com/paper/2509.02544/) (2025) - [OmniParser for Pure Vision Based GUI Agent](https://mlsanity.com/paper/2408.00203/) (2024) - [Mobile-Agent-v3: Fundamental Agents for GUI Automation](https://mlsanity.com/paper/2508.15144/) (2025) - [Agent S: An Open Agentic Framework that Uses Computers Like a Human](https://mlsanity.com/paper/2410.08164/) (2024) - [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024) - [Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents](https://mlsanity.com/paper/2504.00906/) (2025) - [Aria-UI: Visual Grounding for GUI Instructions](https://mlsanity.com/paper/2412.16256/) (2024) - [InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners](https://mlsanity.com/paper/2504.14239/) (2025) - [Scaling Computer-Use Grounding via User Interface Decomposition and Synthesis](https://mlsanity.com/paper/2505.13227/) (2025) - [AutoWebGLM: A Large Language Model-based Web Navigating Agent](https://mlsanity.com/paper/2404.03648/) (2024) - [Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution](https://mlsanity.com/paper/2505.20286/) (2025) - [GTA1: GUI Test-time Scaling Agent](https://mlsanity.com/paper/2507.05791/) (2025) - [InfiGUIAgent: A Multimodal Generalist GUI Agent with Native Reasoning and Reflection](https://mlsanity.com/paper/2501.04575/) (2025) - [ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents](https://mlsanity.com/paper/2508.14040/) (2025) - [CoAct-1: Computer-using Multi-Agent System with Coding Actions](https://mlsanity.com/paper/2508.03923/) (2025) - [OmniParser V2: Structured-Points-of-Thought for Unified Visual Text Parsing and Its Generality to Multimodal Large Language Models](https://mlsanity.com/paper/2502.16161/) (2025) - [UItron: Foundational GUI Agent with Advanced Perception and Planning](https://mlsanity.com/paper/2508.21767/) (2025) - [ScaleTrack: Scaling and back-tracking Automated GUI Agents](https://mlsanity.com/paper/2505.00416/) (2025) - [From Autonomous Agents to Integrated Systems, A New Paradigm: Orchestrated Distributed Intelligence](https://mlsanity.com/paper/2503.13754/) (2025) - [Evolving in Tasks: Empowering the Multi-modality Large Language Model as the Computer Use Agent](https://mlsanity.com/paper/2508.04037/) (2025) ## Built on by (most cited first, 8 of 8) - [OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agent](https://mlsanity.com/paper/2601.07779/) (2026) - [Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving](https://mlsanity.com/paper/2603.18897/) (2026) - [Scaling Agents for Computer Use](https://mlsanity.com/paper/2510.02250/) (2025) - [Video-Based Reward Modeling for Computer-Use Agents](https://mlsanity.com/paper/2603.10178/) (2026) - [OS-Marathon: Benchmarking Computer-Use Agents on Vast-Horizon, Repetitive Tasks](https://mlsanity.com/paper/2601.20650/) (2026) - [MacAgentBench: Benchmarking AI Agents on Real-World macOS Desktop](https://mlsanity.com/paper/2606.22557/) (2026) - [LocalLSTC: A Long Short-Term Control Architecture for Locally Deployed GUI Agents](https://mlsanity.com/paper/2608.25777/) (2026) - [AnyAct: Universal Action for Self-Evolving Agents](https://mlsanity.com/paper/2609.37025/) (2026) ## Abstract (arXiv) Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce Agentic Lybic, a novel multi-agent system where the entire architecture operates as a finite-state machine (FSM). This core innovation enables dynamic orchestration. Our system comprises four components: a Controller, a Manager, three Workers (Technician for code-based operations, Operator for GUI interactions, and Analyst for decision support), and an Evaluator. The critical mechanism is the FSM-based routing between these components, which provides flexibility and generalization by dynamically selecting the optimal execution strategy for each subtask. This principled orchestration, combined with robust quality gating, enables adaptive replanning and error recovery. Evaluated officially on the OSWorld benchmark, Agentic Lybic achieves a state-of-the-art 57.07% success rate in 50 steps, substantially outperforming existing methods. Results demonstrate that principled multi-agent orchestration with continuous quality control provides superior reliability for generalized desktop automation in complex computing environments.