MegaAgent: Autonomous Multi-Agent Systems

• MegaAgent is a large-scale autonomous multi-agent system that dynamically decomposes tasks and coordinates hundreds to thousands of agents without predefined SOPs.
• It employs hierarchical task splitting, parallel execution, and rigorous inter-agent communication protocols (e.g., JSON/XML) to optimize performance and efficiency.
• The architecture features modularity, self-monitoring, and experience pack-driven adaptation, enabling robust scalability and real-world applicability across diverse domains.

MegaAgent refers to a class of large-scale, autonomous multi-agent systems—primarily driven by LLMs but inclusive of heterogeneous agent technologies—that realize dynamic task decomposition, parallel execution, inter-agent coordination, and self-monitoring without reliance on predefined standard operating procedures (SOPs). Recent works including "MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs" (Wang et al., 19 Aug 2024), as well as closely related platforms such as AgentOrchestra (Zhang et al., 14 Jun 2025), Magentic-One (Fourney et al., 7 Nov 2024), GoalfyMax (Wu et al., 13 Jul 2025), $Agent^2$ (Wei et al., 16 Sep 2025), and AgentRec (Ma et al., 2 Oct 2025), define the landscape of MegaAgent frameworks capable of tackling complex, multidisciplinary tasks by orchestrating hundreds to thousands of autonomous agents. These systems advance autonomy, scalability, and flexibility far beyond traditional agent-based paradigms.

1. Architectural Principles

MegaAgent systems are predominantly organized as hierarchical multi-agent frameworks. A central agent (often called the “boss agent,” “planning agent,” or “orchestrator”) receives a complex, natural language input and dynamically decomposes the task into a multi-level tree of subtasks. Each subtask is managed by an admin or coordinator agent, which further divides the work and recruits subordinate agents for granular functions (e.g., code writing, analysis, or file operations).

Typical architectural components include:

• Multi-Level Task Splitting: Recursive, dynamic decomposition by the boss agent, assignment of subtasks to admin agents, and further delegation to subordinate agents.
• Parallel Action Modules: Multiple agent groups operate concurrently, each agent maintains its own checklist and records progress.
• Persistent Storage Modules: Integration of external file systems, code repositories (e.g., via Git), memory databases (vector embeddings with cosine similarity), and execution logs.
• System Monitoring Mechanisms: OS and admin agents monitor agents using checklists and outputs, ensuring verification and enabling runtime updates.
• Communication Protocols: Strict message-passing formats (XML-like, JSON for function calls), supporting parallel dispatch of instructions and standardized agent-to-agent (A2A) interaction (e.g., via the Model Context Protocol in GoalfyMax (Wu et al., 13 Jul 2025) and $Agent^2$ (Wei et al., 16 Sep 2025)).

For example, in MegaAgent (Wang et al., 19 Aug 2024) the boss agent splits a natural language prompt into subtasks, each admin agent decomposes its assigned work further, and subordinate agents execute specific portions—communicating through well-defined formats and monitored via checklists and message logs.

2. Dynamic Task Decomposition and Execution

Central to MegaAgent operation is the dynamic task decomposition process:

• Upon receiving a natural language prompt, the boss agent generates prompts for admin agents specifying roles and task descriptions.
• Admin agents recursively create subordinate agents if their subtask is too complex, invoking function calls (e.g., add_agent).
• Each agent individually records granular steps and maintains a progress checklist for self-monitoring and external verification.
• Large-scale parallelism is enabled by allowing multiple groups of agents to operate without mutual blocking. An ablation paper in (Wang et al., 19 Aug 2024) demonstrated that parallel group execution reduced task completion time for Gobang game development from 4505 seconds to 800 seconds.
• Role-based dynamic allocation applies in multi-domain tasks (e.g., national policy simulation), where 590 agents are recruited and coordinated in a three-level hierarchy.

This recursive, on-demand agent generation avoids rigid SOPs; each agent receives a tailored role and runs autonomously within the defined scope, adapting task plans dynamically as subtasks are completed or if failures are detected.

3. Communication and Self-Monitoring Strategies

Effective inter-agent communication and progress monitoring are vital:

• Agents transact over explicit protocol layers: XML-based dialogs and JSON-encoded function calls (file operations, agent recruitment, command dispatch).
• The communication cost in hierarchical architectures scales as $O(\log n)$, contrasting with traditional $O(n)$ serial frameworks (see (Wang et al., 19 Aug 2024)).
• Monitoring combines hierarchical and flat mechanisms: OS agents validate output format and correctness, admin agents review subtasks, and subordinate agents check their local “TODO” lists.
• Storage modules integrate external files, version-controlled code repositories, and vector memory databases that support retrieval and context management.

GoalfyMax (Wu et al., 13 Jul 2025) formalizes this as a layered communication protocol (MCP), supporting both real-time feedback and asynchronous coordination. Experience Pack architectures are layered for robust knowledge retention: short-term and long-term memory modules, combined with trust-based experience fragments, facilitate continual learning and error avoidance.

4. Scalability, Autonomy, and Adaptation

MegaAgent frameworks demonstrate substantial advancements in system scalability and agent autonomy:

• Hierarchical agent orchestration enables efficient scaling. In policy simulation, MegaAgent coordinated 590 agents with manageable communication overhead (Wang et al., 19 Aug 2024).
• The removal of predefined SOPs allows agents to self-organize, recruit additional members, and adapt plans based on checkpointed progress or detected failures.
• Agent replacements and dynamic role allocation (cf. RLFA (Liu, 29 Jan 2025)) ensure sustained performance, with underperforming agents substituted by superior candidates after probationary evaluation.
• Mixture-of-Experts models, as seen in RLFA, allow agents to internally delegate subtasks to specialized sub-models, enhancing adaptability and overall system efficiency.

GoalfyMax (Wu et al., 13 Jul 2025) and $Agent^2$ (Wei et al., 16 Sep 2025) exemplify protocol-driven adaptation, with experience packs and continual training feedback facilitating self-improvement and reusability across domains. $Agent^2$ automates full RL agent design by decomposing the pipeline into MDP modeling and algorithmic optimization, closing the loop with adaptive feedback analysis.

5. Performance and Empirical Benchmarks

MegaAgent systems are empirically validated through complex task benchmarks:

• In (Wang et al., 19 Aug 2024), MegaAgent completed a full Gobang game (error-free, with both user and AI move support and correct termination logic) in 800 seconds using 7 agents, outperforming MetaGPT and AgentVerse.
• National policy simulation demonstrated the framework’s capacity for scaling up to 590 agents and producing comprehensive multi-domain policy documents in 2991 seconds—far beyond the coordination capabilities (under 10 agents) of existing frameworks.
• AgentOrchestra achieved superior accuracy on multi-modal benchmarks: 95.3% on SimpleQA, robust performance across all levels of GAIA, and higher reasoning accuracy on HLE (Zhang et al., 14 Jun 2025).
• $Agent^2$ led to up to 55% reward improvements in reinforcement learning benchmarks including MuJoCo, MetaDrive, MPE, and SMAC (Wei et al., 16 Sep 2025).
• AgentRec (Ma et al., 2 Oct 2025) produced a 2.8% improvement in dialogue success rate, 1.9% gain in recommendation accuracy (NDCG@10), and 3.2% increase in efficiency, outperforming state-of-the-art conversational recommenders.

A plausible implication is that hierarchical agent frameworks, protocol-driven communication, dynamic role allocation, and experience-based adaptation are key contributors to enhanced task success, scalability, and resource efficiency in large-scale agentic systems.

6. Modularity, Extensibility, and Real-World Applications

MegaAgent designs are inherently modular and extensible:

• Agents and tools can be inserted or removed without retraining, as evidenced by Magentic-One (Fourney et al., 7 Nov 2024), which demonstrates that agent team compositions can be flexibly adapted for different task sets.
• Separation of agent, tool, and model layers allows targeted development, debugging, and deployment of specialized capabilities (web crawling, coding, file operations, multimodal analysis).
• Experience Pack systems (GoalfyMax (Wu et al., 13 Jul 2025)) enable memory reuse and progressive learning, while frameworks such as $Agent^2$ standardize agent generation across diverse environments.
• Applications range from scientific data analysis (multi-agent cosmology pipeline (Laverick et al., 30 Nov 2024)) to adaptable enterprise orchestration, e-commerce recommenders (AgentRec (Ma et al., 2 Oct 2025)), data-driven research, and robust fraud detection (RLFA (Liu, 29 Jan 2025)).

These findings highlight that modularity, interoperability via standard protocols, and layered memory systems are essential to deploying MegaAgent systems across scalable, real-world domains.

7. Future Directions and Potential Challenges

MegaAgent research suggests several promising development directions:

• Enhanced autonomy through removal of rigid pre-scripted SOPs and adoption of self-organizing role/decomposition protocols (Wang et al., 19 Aug 2024).
• Lifelong learning and experience reuse driven by layered memory architectures and continual feedback (Wu et al., 13 Jul 2025).
• Increased safety and robustness, with dynamic validation layers and error-triggered re-planning as seen in ledger/loop architectures (Magentic-One (Fourney et al., 7 Nov 2024), AgentOrchestra (Zhang et al., 14 Jun 2025)).
• Further research into efficient agent replacement, market-like agent pools (RLFA), and closed-loop training feedback mechanisms.
• Addressing integration complexity, cost management (token and computational usage), and retrieval limitations, especially for high-stakes or domain-specific analyses (multi-agent cosmology (Laverick et al., 30 Nov 2024)).

A plausible implication is that as MegaAgent systems continue to grow in complexity and reach, future work will need to address trade-offs between resource efficiency, coordination overhead, memory management, and safety validation for deployment in diverse settings.

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MegaAgent defines the present frontier in scalable, autonomous, and adaptive multi-agent systems. By fusing dynamic hierarchy, modularity, robust protocol-driven inter-agent communication, continual self-improvement, and extensible architecture, it provides the foundation for next-generation agentic platforms—capable of workflow automation, scientific analysis, intelligent recommendation, and more—without the limitations of static, handcrafted process logic.