Agent S: Autonomous LLM Systems

• Agent S is a family of LLM-based autonomous systems designed for complex multi-step task automation through hierarchical and compositional planning.
• These frameworks utilize explicit memory management, multimodal perception, and experience-augmented planning to enhance task success and fault tolerance.
• They enable applications from GUI automation and customer service SOP execution to decentralized multi-agent collaboration in dynamic, partially observable environments.

Agent S refers to a class of agentic frameworks and systems that leverage LLMs for autonomous, multi-step task automation, often targeting complex workflows such as standard operating procedures, open-ended desktop GUI control, or multi-agent collaboration. The term encompasses several research efforts, notably the Agent S framework for GUI automation, the Agent-S workflow for SOP automation, the compositional Agent S2 architecture, and the S-Agents paradigm for self-organizing multi-agent systems. Collectively, these proposals are distinguished by their LLM-centric decision making, hierarchical or compositional agent structures, experience-augmented planning, automatic memory management, and explicit component separation for reasoning, execution, grounding, and collaboration.

1. LLM-Centric Agentic Architectures

The central theme in Agent S research is leveraging LLMs as the core reasoning component for agents that perform nontrivial, multi-step decision making and execution in dynamic, partially observable environments. This is instantiated at multiple levels of abstraction and application:

• Agent S for GUI Automation: This architecture employs experience-augmented hierarchical planning, combining retrieval from external sources (web search), internal episodic/narrative memories, and multimodal perception for robust desktop task automation via GUI manipulation. Primitive action primitives are strictly constrained (e.g., click, type), with the perception stream combining screenshot pixels, accessibility trees, and OCR (Agashe et al., 2024).
• Agent-S for SOP Automation: Agent-S automates customer-care standard operating procedures by decomposing each workflow into sequences of user questions, API calls, and messages, orchestrated through a closed loop involving three specialized LLMs: a State-Decision-LLM, an Action-Execution-LLM, and a User-Interaction-LLM. This separation allows for stepwise, interpretable SOP enactment with explicit execution memory and fault tolerance (Kulkarni, 3 Feb 2025).
• Agent S2: Compositional Generalist-Specialist Model: Targeting GUI task completion, Agent S2 further modularizes agent cognition through a compositional split between high-level generalist "Manager" and "Worker" models (prompted LLMs), which delegate perceptual grounding to a mixture-of-experts ensemble—visual, textual, and structural grounding specialists. This mixture enables robust cross-application GUI action selection, with proactive hierarchical replanning at every subgoal (Agashe et al., 1 Apr 2025).
• S-Agents: Self-Organizing Decentralized Agents: S-Agents extend the paradigm to open-ended multi-agent collaboration with a dynamic "tree of agents" topology, emergent decomposition, and asynchronous coordination. Here, LLMs are embedded in individual roles (root vs. leaves), equipped for local planning and joint hierarchical goal enactment with non-blocking message-passing (Chen et al., 2024).

2. Hierarchical and Compositional Planning Mechanisms

A key technical innovation is the explicit separation of planning into hierarchical and compositional layers, with decision-making distributed across several specialized models or agentic roles:

• High-level to Low-level Planning: In Agent S and Agent S2, a high-level Manager LLM decomposes the global instruction into sequenced subtasks or subgoals. Each subtask is then handled by a Worker, which may further decompose it into atomic GUI actions (Agashe et al., 2024, Agashe et al., 1 Apr 2025).
• Chain-of-Thought SOP Parsing: In Agent-S, the State-Decision-LLM interprets indented plain-text workflows as implicitly structured control flow, reasoning via chain-of-thought to identify current and next actions. Control is advanced based on the execution memory and parsed SOP logic using semantic search over prior actions (Kulkarni, 3 Feb 2025).
• Experience-Augmented Retrieval: Agent S incorporates both external web-level retrieval (via search engines) and internal episodic/narrative memory retrieval (vector database indexed by textual embedding), fusing their results for improved task planning. Ablations confirm that disabling web retrieval (-9.3% absolute) or episodic memory (-7.7%) materially decreases success rates (Agashe et al., 2024).
• Proactive Hierarchical Replanning: Agent S2 dynamically re-plans subgoal lists after every subgoal execution, rather than waiting for failure, boosting performance on OSWorld by up to +6.15 pp on 50-step tasks (Agashe et al., 1 Apr 2025).

3. Memory, Execution Management, and Environment Interfaces

Agent S class systems manage execution history and environmental feedback through explicit memory modules and action/reward interfaces:

• Execution Memory: In Agent-S, an execution memory records the sequence of triples (action, observation, feedback), appended after every step. Decisions are then a function of the current SOP, execution memory, and state LLM output:

$$\pi(a | s, m) \propto \mathrm{LLM}_{\text{state}}([SOP; m; s])$$

with action lookup by embedding similarity to the Global Action Repository (GAR) (Kulkarni, 3 Feb 2025).

• Self-Evaluation and Continual Memory: Agent S probed the material benefit of continual self-supervised memory update via a Self-Evaluator. Removing self-evaluation or continual update reduces OSWorld success by −7.7 pp or −3.1 pp, respectively (Agashe et al., 2024).
• Agent–Computer Interface (ACI): To bound the action/primitives vocabulary and align multimodal perception, Agent S defines an explicit interface mapping LLM-initiated actions to OS-level operations. At each step, Workers receive both screenshots and accessibility trees (augmented with OCR), then issue primitive actions, which the ACI executes and reports labeled feedback (Agashe et al., 2024).
• Mixture-of-Experts Grounding: Agent S2 delegates GUI element localization to three experts (visual, textual/OCR, structural). Ablation studies confirm reliability gains: removing the OCR expert degrades subtask success from 70.6% to 65.2%; omitting structural grounding reduces success from 73.7% to 69.4% (Agashe et al., 1 Apr 2025).

4. Fault Tolerance, Failure Recovery, and Collaboration

Agent S approaches are notable for integrating explicit fault tolerance and robust recovery logic, addressing the noise and error-proneness of real-world environments:

• SOP-Specific Fault Handling: Agent-S leverages the State-Decision-LLM to identify failure causes, perform semantic search for prior steps, and either repeat the most logically dependent action or branch into external knowledge acquisition upon user queries. Loops (same action >2 times) trigger safe termination with engineered grace (Kulkarni, 3 Feb 2025).
• Asynchronous, Decentralized Coordination: S-Agents formalize a tree-structured (ToA) "leadership/root–leaves" architecture permitting non-blocking, asynchronous individual operation. Leaves act independently, reporting upward; the root reassigns or rescales upon failures. Locks and resource allocation are centrally managed, but without synchronous bottlenecks (Chen et al., 2024).
• Emergent Learning and Adaptation: Agent S supports continual memory gathering and self-evaluation, implying an ongoing improvement in long-horizon and non-deterministic planning. In S-Agents, the tree may dynamically grow (spawn leaves), though full realization of dynamic self-modification is left to future work (Chen et al., 2024).

5. Empirical Evaluation, Benchmarks, and Ablation Results

The empirical assessment of Agent S systems is multi-faceted, covering both synthetic, live, and ablation-based evaluation on benchmark tasks.

• Customer Service SOP Automation: Agent-S demonstrated state-decision accuracy of 0.978 with ChatGPT-4o-mini versus 0.565 for 3.5; action generation and parameter extraction were near-perfect (≥0.98), and live chat sessions reproduced high success rates (Kulkarni, 3 Feb 2025).
• Desktop GUI Automation: On the OSWorld benchmark, Agent S with GPT-4o achieved 20.6% overall success (versus 11.2% for GPT-4o baseline), with similar uplifts on WindowsAgentArena (NAVI baseline 13.3%, Agent S 18.2%). Ablations confirm cumulative value of web retrieval, internal memory, ACI-constrained action, and continual update (Agashe et al., 2024).
• Generalist-Specialist for Computer Use: Agent S2 achieved SOTA on OSWorld (15-step: 27.0%, 50-step: 34.5%), outperforming UI-TARS-72B-DPO and Claude CUA by substantial margins. On WindowsAgentArena and AndroidWorld, S2 outperformed baselines by 52.8% and 16.5% relative, respectively. Removal of grounding specialists and proactive replanning each caused multi-point performance drops (Agashe et al., 1 Apr 2025).
• Multi-Agent Collaboration: In Minecraft, the S-Agents "tree of agents" structure enabled faster task completion and lower LLM call counts compared to chain and graph structures. For example, mining 50 stones with ToA(3) took 7.5 min with mean prompt times (mPT) of 3.8, outperforming chain (12.4 min, 9.1 mPT) and graph (10.8 min, 7.7 mPT). ToA(3) reduced solo time by 5–7 minutes per collection task (Chen et al., 2024).

6. Modularity, Portability, and Agent Specification Ecosystems

A recurring concern is the proliferation of agentic frameworks, APIs, and execution models. To promote interoperability and reproducibility:

• Open Agent Specification (Agent Spec): Agent Spec offers a declarative, grammar-defined specification language for agent workflows, supporting JSON/YAML serialization and runtime translation across platforms such as WayFlow, LangGraph, and AutoGen. This neutral format encodes agents, tools, flows, nodes, and data/control edges, with an extensible type system and operational semantics (Benajiba et al., 5 Oct 2025).
• Cross-Framework Workflow Mapping: Agent Spec formalizes translation functions $$\mathcal{T}_F: \mathrm{AgentSpec} \to \mathrm{Spec}_F$$ per framework, enabling "define once, run anywhere" design. Example: mapping an LLMNode to LangGraph involves a runtime-invocable LangGraphLLMStep. Limitations include initial learning curve and evolving support for distributed/concurrent agent patterns (Benajiba et al., 5 Oct 2025).
• Recommended Engineering Practices: Standardization of data-flow, control-flow, validation, and explicit I/O declarations are recommended for robust agent workflow engineering and CI/CD automation (Benajiba et al., 5 Oct 2025).

7. Limitations, Failure Modes, and Future Directions

Despite measurable advances, current Agent S systems share several open challenges:

• Grounding Robustness: In both Agent S and S2, perception/grounding errors remain the leading cause of failure, although their rate is decreasing as specialist modules are adopted (Agashe et al., 2024, Agashe et al., 1 Apr 2025).
• Action and Latency Overhead: High action counts and wall-clock latency remain problematic, especially for long-horizon or exploratory tasks (Agashe et al., 2024).
• Generalization and Adaptivity: Cross-domain transfer is only partially realized; adaptation to new applications sometimes incurs performance degradation, although the experience-augmented approach appears to mitigate this trend (Agashe et al., 2024, Agashe et al., 1 Apr 2025).
• Self-Organizing Expansion and Coordination: The statically determined size of S-Agents teams (leaves at startup) and bottlenecks at roots are open research themes. The field is exploring cost-aware planning, dynamic topologies, and reinforcement-learned negotiation for resource management (Chen et al., 2024).
• Interoperability Gaps: Some frameworks offer specialized features (e.g., concurrent multi-agent planning) not yet encodable in Agent Spec, suggesting the need for further language and ecosystem evolution (Benajiba et al., 5 Oct 2025).
• Recommendations for Progress: Directions include scaling self-supervised continual learning, enhancing perceptual modules, expanding the Agent Spec library for new memory and planning primitives, and integrating richer open-source LLM backbones (Agashe et al., 2024, Benajiba et al., 5 Oct 2025).

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Agent S, in both singular and plural forms, designates a family of LLM-based agent systems and ecosystem specifications that are pushing the frontiers in autonomously enacting complex, interactive workflows in customer support, desktop computing, and collaborative multi-agent environments, with a strong emphasis on modularity, reasoning transparency, experience-augmented planning, and environment-agnostic portability (Agashe et al., 2024, Kulkarni, 3 Feb 2025, Agashe et al., 1 Apr 2025, Benajiba et al., 5 Oct 2025, Chen et al., 2024).