Core-Agent Modularization

• Core-agent modularization is the principled decomposition of intelligent agent systems into reusable modules with minimal coupling and maximal extensibility.
• It employs formal models and universal interfaces to clearly define module interactions, ensuring agent-number independence and robust scalability.
• Empirical evaluations show that modular architectures enhance task success, reduce resource footprints, and enable dynamic reconfiguration in diverse multi-agent systems.

Core-agent modularization refers to the principled decomposition of intelligent agent architectures into distinct, reusable modules, each encapsulating a well-defined subset of agent functionality. This paradigm seeks to achieve minimal coupling between modules, maximized extensibility, and rigorous separation of concerns. Modularization is a key enabling factor for adaptability, composability, large-scale integration, verification, and decentralized coordination in contemporary multi-agent systems and agent frameworks.

1. Formal Foundations of Core-Agent Modularization

The modularization of agents is typically grounded in precise formal models that specify interfaces, state transitions, and patterns of inter-module interaction. The Modular Interpreted System (MIS) framework is one such formalism—an MAS is defined as a tuple $S = (\Agtnames, \Act, \In, \Agt)$, where agents expose modules with finite local states, action sets, and two interference functions: $out$ determines the tokens (messages) an agent emits, while $in$ maps multisets of incoming tokens to local impressions. Critically, revised MIS decouples interference functions from the total number of agents, facilitating agent-number independence and verifiable openness. Interaction complexity ($IC(S)$) and the degree of openness are formally quantified; multi-agency requires LOGTIME-sparse interaction and modular expansion requiring at most $O(1)$ or $O(0)$ rewrites per agent (Jamroga et al., 2013).

Many frameworks adopt alternative, but functionally similar, interface-centric decompositions. The EIGHT platform formalizes module independence via the Impact-Scope metrics, where independence is operationalized as the absence of causal dependency chains between modules under all contexts ($$s_m(scope(p(c(\{M_i\}), R(x)))) \cap \{M_j\} = \emptyset$$). Universal interfaces (SCI) are used as module boundaries, ensuring complete decoupling even in monolithic deployments (Wang et al., 6 Nov 2025).

2. Architectural Patterns and Module Taxonomies

A core-agent is typically a composite of several abstract modules representing orthogonal agent concerns. The LLM-Agent-UMF framework, applied across LLM-powered agents, standardizes five canonical modules: planning, memory, profile, action, and security—expressed as a tuple $$\mathcal{C} = \langle \Pi, M, \Phi, A, \Sigma \rangle$$ (Hassouna et al., 2024). Planning is responsible for goal decomposition and strategy synthesis; memory manages state and context; profile modulates the system persona; action executes plans or API calls; security enforces confidentiality, integrity, and safety constraints.

Most contemporary frameworks reflect similar modular stratification:

• Amico (event-driven agents): Event handling, state management, behavior execution, reasoning integration (Yang et al., 19 Jul 2025).
• Agent-Kernel (microkernel MAS): Agent, environment, action, controller, plus plugin subsystems for cognition, capability, and relation (Mao et al., 1 Dec 2025).
• MOD-X (decentralized exchange): Universal message bus, state management, translation/adapters, blockchain security (Ioannides et al., 6 Jul 2025).
• MCP-Server for BIM (microservices): mcp-server, bim-exec, bucket, knowledge, viewer—decoupled via abstract adapter contracts (Heimig-Elschner et al., 21 Dec 2025).

LLM-based systems increasingly distinguish core modules (plan/memory/security) from tool and LLM components, defining active (full-module) and passive (action-only/guardrail) core-agents; multi-agent orchestration leverages uniform or hybrid deployment of these agents (Hassouna et al., 2024).

3. Interface Abstraction, Decoupling, and Independence

Achieving true modularity requires rigorous abstraction of module interfaces and elimination of deep dependency chains. Universal interfaces (SCI in EIGHT) or abstract contract definitions (BaseAdapter in MCP-Server) establish fixed I/O vocabularies—processors of various arity (IProcessor<In, Out>), resource accessors (IInputResource, IOutputResource), extension markers (IExtendable)—through which all modules communicate (Wang et al., 6 Nov 2025, Heimig-Elschner et al., 21 Dec 2025).

Module independence is quantitatively assessed via impact-scope functions that trace the propagation of change events through dependency rules. Absolute independence is achieved when all impact-scope projections from a module's internal changes map strictly to that module, for all contexts (Wang et al., 6 Nov 2025). Systems such as EIGHT and MOD-X further enforce independence through runtime binding, hot-swap adapters, and a strict separation between module implementation and connection logic.

4. Dynamic Composition, Coordination, and Openness

Core-agent modularization is not static; frameworks must support dynamic loading, unloading, and reconfiguration of modules. EIGHT achieves this via OSGi bundles and a persistent binding/linker engine with runtime adapters; practitioners can add, update, or remove agent modules by manipulating directory contents and binding configurations, with zero effect on core services or other agents (Wang et al., 6 Nov 2025).

Coordination and interaction are externalized through event- or message-driven protocols:

• DECOMAS defines activated coefficient modules that minimally invade agent cores, observing reasoning events and injecting new events via declarative mappings—coordination is thus plug-and-play, with traceable event mappings (Sudeikat et al., 2010).
• MOD-X utilizes a universal message bus, topic-based pub/sub, and translation/adapters for cross-agent workflows; dynamic workflow orchestration is modeled as DAGs, robust to agent heterogeneity and failures (Ioannides et al., 6 Jul 2025).
• Macsy leverages indirect stigmergic coordination via a shared blackboard architecture, scheduling modules by tag-triggered pulls rather than direct invocation; modules are oblivious to each other's existence (Flaounas et al., 2014).

Openness is formally defined as the minimal number of module rewrites required for system expansion or reduction; optimal modular systems achieve agent-number independence in their interference functions, allowing for $O(0)$-open designs (Jamroga et al., 2013).

5. Methodologies for Modularization: Guidelines and Recipes

Modular agent construction is a systematic process. Key methodological steps include:

1. Enumerate orthogonal functions (perception, planning, memory, actuation, security).
2. Specify each function as an abstract interface—fixed I/O contract, stateless if feasible.
3. For multi-agent systems, encode agent-to-agent interactions using limited vocabularies—tokens/messages or universal interface tuples.
4. Require connectors or adapter scripts for all module bindings at compile- or assemble-time.
5. Employ platforms with component managers, plugin lifecycles, and runtime linkers for hot-swappability.
6. Evaluate sparse interaction and degree of openness; validate against formal criteria for multi-agency and expansion cost.

Many frameworks advocate the Open/Closed Principle: new capabilities are captured as new modules/components, never as changes to old modules; agent behavior is extended solely by attaching components (ECS in HECATE (Casals et al., 8 Sep 2025), plugin swapping in Agent-Kernel (Mao et al., 1 Dec 2025), adapter addition in MCP-Server (Heimig-Elschner et al., 21 Dec 2025)).

6. Empirical Evaluation, Extensibility, and Impact

Quantitative and qualitative evaluations demonstrate the impact of core-agent modularization on extensibility, reliability, and scalability:

• Amico: Task success increased by 29.5% over baseline; resident memory footprint reduced by orders of magnitude; near-instant cold starts in embedded and browser contexts (Yang et al., 19 Jul 2025).
• MCP-Server: 100% tool success rate, 40–100% model acceptability, zero code coupling between agent and backend APIs (Heimig-Elschner et al., 21 Dec 2025).
• LLM-Agent-UMF: Modularity ×1.4, extensibility ×1.5 after core-agent framework retrofits; security scores increased via guardrail module addition (Hassouna et al., 2024).
• MOD-X: Seamless workflow execution among heterogeneous agents, decentralized trust and global recoverability (Ioannides et al., 6 Jul 2025).
• Modular RL: Fast method swapping, distributed training scheme reconfiguration with minimal code churn (Bou et al., 2020).
• Arcade module learning: 1,000× sample efficiency gain, human-level game play within 10–15 minutes, modular causal decomposition exhibiting superior generalization (Melnik et al., 2019).
• Macsy: Million-scale data pipeline, emergent annotation workflows, complete fault isolation per module (Flaounas et al., 2014).

7. Application Domains and Future Directions

Core-agent modularization underpins contemporary advances in agentic LLM tool use, persistent autonomy in edge devices, multi-agent social simulation, decentralized exchange, and interpretable workflow orchestration. The approach is foundational to scalable agents in BIM, RL, speech-processing (modular program generation), and hybrid reasoning architectures. Emphasis is shifting toward dynamic, event-driven, composable, and provably independent system cores (EIGHT, MOD-X, Agent-Kernel), with increased attention to formal guarantees, security, and integrated multi-core or multi-agent scenarios.

Challenges remain in automating optimal decomposition, tracking emergent coordination, balancing openness and security, and evolving module taxonomies for new agent capabilities. Practitioners increasingly rely on universal interface patterns, dynamic loaders, runtime binding, and contract-based module definitions to guarantee robustness and facilitate future growth.