Modular Multi-Agent Systems

• Modular multi-agent systems are frameworks that break down complex tasks into distinct, interoperable agent modules for specialized functions.
• They leverage standardized communication protocols and memory architectures to boost scalability, robustness, and dynamic coordination in various applications.
• Empirical studies show that modular designs improve workflow planning, reduce errors, and enhance adaptability across diverse operational domains.

A modular multi-agent system (MAS) is an engineered, often software-centric framework in which a collection of agents—each encapsulating specific functions, expertise, protocols, or cognitive roles—are organized into separable, well-defined modules. Modularity in MAS refers not just to codebase structure but to the decomposition of system responsibilities, communication protocols, memory management, and execution pipelines, enabling scalability, robustness, extensibility, and clear separation of concerns. Recent MAS research combines modular software design, role specialization, communication primitives, and hierarchical orchestration, supported by empirical evidence across scientific reasoning, laboratory automation, workflow planning, communication, and evidence synthesis.

1. Foundational Principles and Formal Definitions

Modular MAS architectures enforce agent decomposition, clean interface specification, and plug-and-play extensibility. A modular agent module can be defined as a tuple

$A_i = (I_i, P_i, O_i)$

where $I_i$ is the set of inputs, $P_i$ is the agent’s policy pipeline, and $O_i$ is the set of outputs (Qiu et al., 2 Jul 2025). Modules communicate over standardized channels (JSON-RPC, HTTP/gRPC, message brokers, or custom protocols), with each module registered at orchestration startups. For example, the M-Reason system defines agent modules as

$$m = \langle I_m, O_m, S_m, \Delta_m, \Sigma_m \rangle$$

where $\Delta_m$ is the (possibly stochastic) transition function and $\Sigma_m$ the I/O schema (Wysocki et al., 6 Oct 2025).

A general property of a modular MAS is openness: the ability to join or remove agents without rewiring the interfaces of others, as formalized in modular interpreted systems (MIS) by changing interference functions from tuple- to multiset-based, reducing the dependence on the total agent count (Jamroga et al., 2013).

2. Canonical Modular Multi-Agent System Architectures

Several exemplary instantiations of modular MAS span domains:

• Hierarchical MAS with role specialization: BioMARS employs a Biologist Agent (protocol synthesis), Technician Agent (code translation), and Inspector Agent (perceptual anomaly monitoring), each with a precise policy and message protocol, together executing laboratory automation workflows (Qiu et al., 2 Jul 2025).
• Modular memory systems: MIRIX splits long-term memory into six agent-managed components (Core, Episodic, Semantic, Procedural, Resource, Knowledge Vault), coordinated by a Meta Memory Manager, with every memory update or query routed via a coarse-then-fine API call sequence (Wang et al., 10 Jul 2025). LEGOMem adds procedural memory for orchestrator and agent roles, allocating full-task and subtask memory units for planning and execution (Han et al., 6 Oct 2025).
• Communication and tool-use modularity: AgentMaster formalizes agent-to-agent (A2A) and Model Context Protocol (MCP) calls, allowing dynamic task decomposition, routing, and synthesis across domain agents (SQL, retrieval, image, general) (Liao et al., 8 Jul 2025). LLM×MapReduce-V3 composes atomic survey-generation servers into a hierarchy managed by a high-level planner agent (Chao et al., 13 Oct 2025).
• Resource and task abstraction: DRAMA unifies agents and tasks as “resource objects” with explicit attributes and lifecycle transitions, leveraging a planner-critic allocation loop and dynamic affinity-based scheduling across a separated control and worker plane (Wang et al., 6 Aug 2025).
• Specialist ensemble systems: MAATS (machine translation) pipelines LLM-based translation followed by parallel MQM-evaluator agents (Accuracy, Fluency, Style, etc.), with refinement by an Editor agent—each role isolated and interpretable (Wang et al., 20 May 2025). MAM applies role-embodied agents for multi-modal medical diagnosis (GP, Specialists, Radiologist, Assistant, Director) (Zhou et al., 24 Jun 2025).
• Evidence synthesis and auditing: M-Reason instantiates a two-level orchestration of parallel evidence-analyzer agents and a report-integration coordinator, combining LLM-based subanalyses and deterministic integration/validation (Wysocki et al., 6 Oct 2025).
• Concurrent, event-driven modularity: CMA has fully asynchronous stateless modules operating on a global vector state with message passing via MQTT, supporting real-time perception and adaptation in robotics settings (Maruyama et al., 26 Aug 2025).

3. Communication and Coordination Protocols

Explicit communication protocols underpin modular MAS operability and scalability:

• A2A (Agent-to-Agent) Messages: Structured as directed graph edges with JSON objects indicating sender, recipient, type, and payload. Routing via shortest-path algorithms on the agent network graph (Liao et al., 8 Jul 2025).
• MCP (Model Context Protocol): Standardizes tool/context function calls, responses include service outputs and context updates $(s, \Delta \mathcal{C})$, critical for tool-oriented and memory-augmented systems (Liao et al., 8 Jul 2025, Chao et al., 13 Oct 2025).
• Parallel pipelines and consensus: For evidence synthesis, MAS favor parallel agent pipelines (run-to-consensus or unanimous approval) with orchestrators mediating feedback and iteration (Wysocki et al., 6 Oct 2025, Wang et al., 20 May 2025).

Hierarchical and feedback-driven scheduling ensures that agent outputs are synthesized coherently (e.g., LLM-based planners dynamically invoke tool modules and aggregate outputs).

4. Modular Memory and Role Allocation

Memory modularization is critical for scaling, interpretability, and effectiveness:

• Specialized memory managers: MIRIX hierarchically compartmentalizes core, episodic, semantic, procedural, resource, and vault memory—each with distinct schemas, scoring, and access patterns (Wang et al., 10 Jul 2025).
• Procedural memory granularity: LEGOMem demonstrates the importance of orchestrator-level (full-task) vs. agent-level (subtask) memory for workflow automation. Memory allocation is task-context and pipeline-stage aware, with significant gains for weak agents (Han et al., 6 Oct 2025).
• Plug-in design: Adding new memory or function managers is a matter of registering an agent with the controller, facilitating domain adaptation and feature extension (Wang et al., 10 Jul 2025).

5. Algorithms and Planning Methods in Modular MAS

Modular MAS utilize compositional, constraint-enforcing, and routing algorithms:

• Retrieval-augmented generation (RAG): Protocol generation and workflow planning integrate search, similarity-based ranking, and logic validation (as in BioMARS’s Biologist Agent and Agentic RAG pipeline) (Qiu et al., 2 Jul 2025).
• Code synthesis and validation: Technician Agents translate natural language protocols into primitive function calls, followed by rule-based validation enforcing API semantics (Qiu et al., 2 Jul 2025).
• Dynamic task scheduling: Affinity-driven bipartite matching and planner-critic loops optimize agent-task assignments under dynamic constraints (DRAMA, (Wang et al., 6 Aug 2025)).
• Compositional RL and predictive planning: Two-tier, fixed-dimension agent hierarchies with compositional critics and exhaust-search action selection accelerate convergence and guarantee safety (Liao et al., 3 Jun 2025).
• Graph-based policy modularization: ModGNN generalizes GCNs to support arbitrary nonlinear modular sub-aggregations in communication-enabled multi-agent policies (Kortvelesy et al., 2021).

6. Performance, Scalability, and Evaluation

Empirical results across systems demonstrate the efficacy of modular MAS:

System	Domain	Key Performance Gains
BioMARS	Cell culture	Protocol error reduction (93%), higher repeatability, 96%+ accuracy
MIRIX	Memory/vQA/Chat	+35% accuracy vs. RAG, 99.9% storage reduction, 410% recall boost
LEGOMem	Workflow	+12.6–13.4 pp success gains on weak/strong agent teams
MAATS	Translation	450% more true errors detected, robust to small or distant LLMs
DRAMA	Dynamic envs	Maintains 100% SR in agent dropout/addition; improves efficiency
SciAgent	Science Olympiads	Matches/surpasses gold-medalist performance across exams

Repeated themes are robust plug-and-play integration, sublinear latency under parallelism, and retention of accuracy or reproducibility over baselines (Qiu et al., 2 Jul 2025, Liao et al., 8 Jul 2025, Wang et al., 10 Jul 2025, Han et al., 6 Oct 2025, Wang et al., 20 May 2025).

7. Extensibility, Interoperability, and Generalization

Modular MAS are explicitly designed to support adaptation to new domains, tool sets, and agent ensembles:

• Plug-in/Service registry models: New modules or servers (memory, retrieval, specialist, critic) can be added under uniform API contracts, registered with orchestration or meta-managers without refactoring existing code (Wang et al., 10 Jul 2025, Chao et al., 13 Oct 2025).
• Domain transfer: Architectures (BioMARS, MAM, AgentMaster, M-Reason) are portable to other scientific, medical, legal, or industrial pipelines by substituting domain corpora and agents or extending base function libraries (Qiu et al., 2 Jul 2025, Wysocki et al., 6 Oct 2025, Zhou et al., 24 Jun 2025).
• Policy/governance abstraction: Governance layers (GaaS) operate externally and model-agnostically, enforcing runtime policies with full auditability and dynamic trust modulation, thereby supporting safe, accountable cross-MAS deployment (Gaurav et al., 26 Aug 2025).
• Formal modularity guarantees: Systems with modular interference functions (MIS), plug-in meta-memory, or explicit communication graphs guarantee high openness and low coupling, supporting agent/system join and leave with zero to O(1) modifications [(Jamroga et al., 2013); (Wang et al., 10 Jul 2025)].

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Modular multi-agent systems embody rigorous, scalable engineering principles—decomposing cognition, memory, communication, execution, and governance into distinct, well-orchestrated units. The empirical and formal evidence across recent research confirms that such architectures enable robust, extensible, and interpretable agent ecosystems deployed in a diverse array of real-world and scientific domains [(Qiu et al., 2 Jul 2025); (Liao et al., 8 Jul 2025); (Wang et al., 10 Jul 2025); (Han et al., 6 Oct 2025); (Chao et al., 13 Oct 2025); (Wang et al., 6 Aug 2025); (Li et al., 11 Nov 2025); (Jamroga et al., 2013)].