Core-Agent Structure

• Core-agent structure is an architectural framework that separates core functionalities like planning, memory, and security from peripheral, tool-centric operations.
• It employs modular roles—such as planning, memory, profile, action, and security—to achieve scalability, resilience, and precise agent orchestration.
• This structural decomposition supports LLM orchestration, reinforcement learning, and decentralized network models, driving efficiency and innovation in complex systems.

A core-agent structure is a principled architectural decomposition that assigns distinct, modular roles within an agentic or multi-agent system, separating core functional responsibilities—such as planning, memory, and security—from subordinate, environment-facing, or tool-centric behaviors. The term refers to a dedicated software or cognitive entity (often distinct from the model executing language or perception tasks) that centrally coordinates policy, goal decomposition, persona, data flow, and action execution. The concept is prominent in state-of-the-art agent frameworks for reinforcement learning, multi-agent collaboration, social modeling, and LLM orchestration. Theoretical models of community formation, multi-agent social networks, and distributed AI control routinely revolve around the distinctions—both architectural and dynamical—between core and non-core (peripheral/passive) agents.

1. Foundational Definitions and Theoretical Motivation

The core-agent concept originates from both formal network theories and software engineering in autonomous and multi-agent systems. In the context of LLM-agent architectures, a core-agent is the "central coordinator that orchestrates interactions among the LLM, tools, and environment, translating high-level goals into executable actions" (Hassouna et al., 17 Sep 2024). This role is orthogonal to tool APIs (which provide domain-specific functionality) and to LLMs as language reasoners (which process context and generate text).

In network science, core-periphery decompositions define system structure in terms of agents with disproportionately high connectivity and influence (core) versus those with sparse or locally clustered ties (periphery). Game-theoretic models refine this to strategic interaction rates and Nash equilibrium structure (see (Su et al., 2022)). In cognitive and agent-based frameworks, core-agent layers model the hierarchical control flow that separates abstract mission, strategic planning, self-modeling, and concrete action execution (Shapiro et al., 2023). Across all formulations, the distinction between core and non-core agents is crucial for understanding system function, resilience, and emergent coordination.

2. Internal Modular Structure of the Core-Agent

A core-agent architecture is decomposed into orthogonal modules via the Single Responsibility Principle, affording clarity and functional separation (Hassouna et al., 17 Sep 2024, Shapiro et al., 2023). The canonical design (as exemplified by LLM-Agent-UMF (Hassouna et al., 17 Sep 2024)) includes five principal modules:

• Planning: Decomposes tasks and generates/corrects execution plans, spanning iterative (e.g., tree search) and non-iterative (chain-of-thought) strategies. Both symbolic (rule-based, e.g., PDDL) and model-based (LLM-driven) planners are supported: $P = f_{\mathrm{plan}}(S, G)$, $P' = f_{\mathrm{revise}}(P, F)$.
• Memory: Manages both short-term and long-term state, with explicit update and retrieval mechanisms: $M_{t+1} = \text{update}(M_t, O_t)$, $R_t = \text{retrieve}(M_t, Q)$.
• Profile: Maintains agent persona, system prompt, and behavioral alignment, supporting handcrafting, self-generation, and weight-based fine-tuning.
• Action: Serializes high-level plan steps into tool/environment interactions. Encompasses both plan-following automation and reactive (API call) invocation.
• Security: Implements stringent guardrails for prompt injection, response filtering, and data privacy, combining rule- and LLM-based strategies.

The granularity and implementation of these modules vary across frameworks but generally align with the ACE six-layer model (Shapiro et al., 2023), which adds explicit layers for aspiration (moral compass), global strategic reasoning, executive function, and cognitive control, culminating in task-specific prosecution.

3. Core-Agent Typologies: Passive, Active, and Multi-Core Architectures

Core-agents are classified by control authority and state management. An Active Core-Agent possesses planning, memory, and profile management, orchestrates system state, and maintains long-term data and agent policy. A Passive Core-Agent is stateless (or short-term only), executing action and security without planning or persona control (Hassouna et al., 17 Sep 2024). This is formalized by indicator variables $\alpha$ (planning) and $\beta$ (memory), with Active if $\alpha = \beta = 1$, Passive if $\alpha = \beta = 0$.

These can be composed into multi-core architectures:

• Uniform Multi-Passive: Parallel, stateless core-agents managed by a single LLM, with negligible synchronization overhead.
• Uniform Multi-Active: Multiple active managers require consensus and state synchronization (e.g., Raft protocols).
• Hybrid One-Active-Many-Passive: A recommended topology where a single active manager delegates subtasks to specialized passive agents, optimizing modularity, availability, and scalability.

This taxonomy directly influences system synchronization, modularity, and security properties, as formalized in architectural tradeoff analyses (Hassouna et al., 17 Sep 2024).

4. Core-Agent Structure in Network and Social Systems

The core-agent paradigm extends to multi-agent social, cognitive, and network-formation models. Core-periphery SBMs in agent-based innovation models show that decentralized cores—dense internal clusters with weak periphery ties—accelerate exploration and innovation by shielding periphery nodes from premature convergence on core-local optima (Milzman et al., 2023). Network formation games yield equilibria in which a small core emerges as a global information aggregator, while periphery nodes establish selective, homophilous local links (Su et al., 2022). Agent-based coalition formation heuristics approximate cooperative-game-theoretic cores by enforcing myopic, local improvement dynamics that emulate coalition rationality and grand coalition efficiency (Vernon-Bido et al., 2020). These dynamics are mathematically grounded using stochastic block models, potential game theory, Nash equilibrium, and hedonic partitioning.

5. Implementation Patterns and Exemplary Frameworks

Core-agent structure permeates a range of agent engineering frameworks:

• HECATE (Casals et al., 8 Sep 2025): Implements ECS-based multi-agent systems, representing core-agents as entities with configurable sets of data-carrying components (belief, goal, intention, messaging) and orthogonal systems responsible for reasoning, planning, grouping, and communication. This yields composability and performance at scale.
• LLM-Agent-UMF (Hassouna et al., 17 Sep 2024): Enforces clear separation of LLM, tool, and core-agent layers, modularizing agent control, planning, profile management, and security. Formalizes the structuring of multi-core agent architectures for scalability and specialization, verified via ATRAF.
• ACE (Shapiro et al., 2023): Articulates a vertical core-agent stack mapping mission, strategy, self-model, execution, and feedback, supporting bidirectional data and control flow.
• CORE-Agent (Siegel et al., 17 Sep 2024): Specializes in computational reproducibility, implementing domain-specific prompts, validation and error recovery atop a generic LLM-agent backbone.
• Mobile and Social Agents: In collaborative pipelines (e.g., CORE (Fan et al., 17 Oct 2025)), core-agent design partitions local and cloud reasoning, enforces privacy-preserving decision pipelines, and modularizes sub-task and action selection.

6. Trade-Offs, Performance, and Design Guidelines

Architectural decisions in core-agent design entail quantifiable trade-offs:

• Modularity and SRP: Strong separation of concerns favors extension and maintainability.
• Scalability: Synchronization-points and state-sharing in multi-active agents negatively affect scalability; hybrid designs minimize this.
• Security: Early embedding of dedicated security modules is mandatory to mitigate risks of data leaks and prompt injection.
• Composability and Loose Coupling: Openness to novel LLM, memory, or guardrail technologies is critical for future-proofing.
• Role Specialization: Passive core-agents are optimal for low-level API mediation; genuine decomposition and stateful reasoning require active agents (Hassouna et al., 17 Sep 2024).

Analyses such as ATRAF optimize architectures for maximal modularity, scalability, and availability, with empirical validation across 13 state-of-the-art systems (Hassouna et al., 17 Sep 2024).

7. Core-Agent Structure in Multi-Agent Teams and Social Innovation

Core-agent structure underpins team organization and innovation in both artificial and human-inspired agent collectives. LLM-powered agent teams exhibit systematic accuracy differentials depending on agent-to-agent structural protocol. Flat, consensus-based teams (i.e., core-less or fully decentralized structures) outperform strict hierarchies in tasks requiring significance integration and corrective feedback, as measured by accuracy metrics and LLM-as-judge evaluations (Muralidharan et al., 8 Oct 2025). Decentralized core-periphery networks, as defined in stochastic block modeling, facilitate more rapid solution of complex collective innovation tasks due to the insulation and selective coupling between core and periphery (Milzman et al., 2023).

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In summary, the core-agent structure is an authoritative organizing principle that partitions agentic intelligence and control within and across agents, enabling rigorous modularization, scalable orchestration, secure execution, and emergent collective function. It is a central abstraction for LLM-tool integration, agentic reasoning, and social or cooperative network architectures, tightly linked to both software and mathematical formalisms in the contemporary agent literature (Hassouna et al., 17 Sep 2024, Shapiro et al., 2023, Casals et al., 8 Sep 2025, Su et al., 2022, Milzman et al., 2023, Muralidharan et al., 8 Oct 2025, Vernon-Bido et al., 2020, Fan et al., 17 Oct 2025, Siegel et al., 17 Sep 2024).