Multi-Expert Modular Agents

Updated 10 December 2025

Multi-expert or modular agents are systems composed of specialized autonomous modules that collaboratively decompose and solve complex tasks.
They enhance scalability, flexibility, and performance across domains such as conversational AI, robotics, and decision support.
Mathematical formulations and routing mechanisms drive their adaptive specialization, efficient task decomposition, and robust extensibility.

A multi-expert or modular agent system comprises a set of autonomous, specialized modules—each acting as an "expert" in a domain, function, or subtask—coordinated under a well-defined protocol to realize collective or composite intelligent behavior. These architectures have become both essential and ubiquitous in complex AI deployments, spanning dialogue systems, knowledge-based reasoning, robotic control, scientific discovery, and decision support. The modular design paradigm enables not only specialized skill encapsulation but also adaptive orchestration, efficient task decomposition, context-sensitive delegation, and robust extensibility. Recent research crystallizes the mathematical foundations, orchestration strategies, communication protocols, and empirical trade-offs governing this modality.

1. Core Definitions and Architectural Patterns

A modular or multi-expert agent system is formally constructed as a finite set of modules or agents $\mathcal{M} = \{A_1, \ldots, A_n\}$ , each with local policy, knowledge base, or inference mechanism specialized for a domain, expertise, or function. At the system orchestration level, two principal paradigms recur:

Parallel ensembles: All experts $A_i$ observe a query or input $Q$ and independently generate candidate outputs $R_i$ , which are then ranked, merged, or routed by a stateless aggregation engine or response selector, as in the "One For All" abstraction in conversational AI (Clarke et al., 13 Jan 2024), MetaQA for multi-domain QA (Puerto et al., 2021), and modular classifiers (0902.2751).
Explicit user selection or delegation: The end-user (or a coordinator agent) explicitly selects a domain expert, e.g., through an agent selection interface or by specifying a target domain, as in Agent Select (Clarke et al., 13 Jan 2024) or modular dialogue frameworks (Pei et al., 2019).
Hierarchical or layered multi-agent frameworks: Agents are organized as layers (e.g., base-level specialists, aggregation agents, meta-reasoners); communication proceeds via staged deliberation, voting, iterative refinement (e.g., MoMoE (Shu et al., 17 Nov 2025), concurrent modular frameworks (Maruyama et al., 26 Aug 2025)).
Coordinator-centric dispatch or central "kernel": A central agent (dispatch kernel) manages which subset of experts processes each query (often via confidence-based or feature-based subsampling) to reduce communication overhead and optimize expert coverage (0902.2751).

Experts may be either "black-box" (no visibility into internals; only input–output signature is known) or "white-box" (the architecture, parameters, or reasoning traces are accessible to the orchestrator).

2. Mathematical Formulations and Routing Mechanisms

Modular agent frameworks formalize expert selection, delegation, or combination as a routing or mixture-of-experts problem. Common paradigms include:

Embedding-based selection: For a user query $Q$ and candidate responses $R_i$ , compute embedding vectors $\mathbf{q} = \operatorname{Embed}(Q)$ , $\mathbf{r}_i = \operatorname{Embed}(R_i)$ and select $i^* = \arg\min_i \|\mathbf{q} - \mathbf{r}_i\|_2$ (Clarke et al., 13 Jan 2024). This black-box approach leverages universal encoders such as BERT or USE and remains agnostic to expert internals.
Gating networks and soft mixtures: Given outputs of $K$ experts $\mathbf{p}_1, \ldots, \mathbf{p}_K$ , a gating network computes non-negative mixture weights $\beta^l_j$ per time step or data instance, yielding the combined output $\mathbf{p}_j = \sum_{l=1}^{K+1} \beta^l_j \mathbf{p}_j^l$ (Pei et al., 2019). The gating network is typically a shallow MLP conditioned on expert states and predictions.
External/learned routers: For modular robotics or code agent ensembles, explicit routers (embedding similarity (Kuzmenko et al., 2 Jul 2025), prompt-driven LLM inference (Kuzmenko et al., 2 Jul 2025)) query natural-language meta-descriptions and decide the routing index $r = f_{\text{route}}(t; \{m_i\})$ .
Coordination protocols: Decision logic may be centralized (global aggregator, coordinator agent), sequential (pipeline runner), or fully decentralized (asynchronous message passing, e.g., MQTT overlays (Maruyama et al., 26 Aug 2025)), depending on trade-offs in latency, robustness, and transparency.

The integration protocol takes the general form:

$\mathcal{Y} = \mathcal{F}\bigl( A_1(Q,\mathcal{S}), A_2(Q,\mathcal{S}), \ldots, A_n(Q,\mathcal{S}) \bigr)$

where $\mathcal{F}$ is an aggregation or fusion operator—often learned (e.g., in MetaQA), or explicitly parameterized.

The architectural decomposition into modular experts is typically motivated by the need to:

Reduce negative transfer: Modular Adaptive Policy Selection (MAPS) demonstrates that, for multi-task imitation, decomposing the policy into task-shared and task-specific sub-behaviors (via softmax gating and regularized module assignment) outperforms fully shared or fully separated architectures (Antotsiou et al., 2022).
Enable scalability and maintainability: Each expert (module) operates independently, allowing natural insertion, removal, or retraining as task boundaries evolve (0902.2751, Puerto et al., 2021, Gesmundo, 2023).
Decompose complex reasoning: Multi-stage expert pipelines reflect real-world analytical protocols, e.g., functional group detection, peak assignment, and molecular graph hypothesis in IR-Agent for chemical structure determination (Noh et al., 22 Aug 2025).
Support on-line learning and adaptation: Feature and concept boundaries in kernel-based classifiers are dynamically refined through local statistics and agent–peer consultations (0902.2751).

A crucial dimension is the selector or router’s regularization to balance sharing, exploration, sparsity, and consistency. MAPS, for example, combines imitation loss with penalties for over-sharing, under-utilization, and temporal inconsistency, ensuring modules capture both common primitives and task-specific nuances (Antotsiou et al., 2022).

4. Communication Protocols and Cross-Agent Consistency

Robust modular systems require communication protocols that are both efficient and inference-preserving:

Feature- or tag-based communication: Systems may use only compact headers or tag lists summarizing object properties, dramatically reducing message payload and enabling selective agent engagement (0902.2751).
Modular logic and uncertainty translation: In structured expert systems, modules may use different uncertainty calculi (e.g., multi-valued logics), necessitating inference-preserving or conservative translation functions between modules. Theoretical guarantees are established via morphisms or quasi-morphisms between logical algebras (Agustí-Cullell et al., 2013).
Natural language and embedding-based interaction: LLM-based modules intercommunicate via natural language over asynchronous messaging backbones (e.g., MQTT), with all persistent knowledge externalized into a shared vector database, supporting emergent intention and fault tolerance (Maruyama et al., 26 Aug 2025).
Hybrid channel structures: Table-driven, key–value store (Redis, embedding index), and blackboard architectures are ubiquitous for synchronizing shared memory, agent annotations, recommendations, and result aggregation (Sorstkins et al., 18 Sep 2025, Maruyama et al., 26 Aug 2025).
Scalable context handling: To bound the growth of message or memory context, modular systems use compressed summaries, local context windows, or summary LLMs to distill and store high-value variables (Yu et al., 22 Dec 2024).

Efficient communication further enables robust extension, such as plug-and-play module addition, and modular error diagnosis—agents’ outputs can be isolated, rated, and improved independently in the context of cognitive diagnostic frameworks (Sorstkins et al., 18 Sep 2025).

5. Empirical Results, Comparative Performance, and Scalability

Systems leveraging modular/multi-expert architectures consistently achieve stronger empirical performance and usability than monolithic or non-modular alternatives, across application domains:

Conversational AI: “One For All” orchestration yields System Usability Scale (SUS) scores of $\mu=86.0$ (vs. $\mu=56.0$ for agent-select), and 71% vs. 57% accuracy in task completion; selected responses are rated within 1% of human-selected ground-truth (Clarke et al., 13 Jan 2024).
Classification and concept learning: Modular kernel architectures outperform fully connected multi-agent baselines both in classification accuracy (over 92% vs. 88%) and in message count by over 20-fold (with 1,000 classes) due to selective agent dispatch (0902.2751).
Multi-domain QA and multitask learning: MetaQA achieves $\sim$ 2–8 point improvements over prior multi-agent and multi-dataset baselines, while requiring only 13–16% of the data needed by monolithic approaches (Puerto et al., 2021). MAPS reduces negative transfer and boosts success rate in >90% of settings vs. meta-learning (Antotsiou et al., 2022).
Real-world agent deployment: Tiered, modular conversational survey agents produce higher completion rates, response quality, and privacy compliance through reusable engineered prompt and RAG modules (Yu et al., 22 Dec 2024). Concurrent Modular Agent architectures (CMA) exhibit emergent planning, intention, and fault-tolerance in embodied hybrid and android settings (Maruyama et al., 26 Aug 2025).
Parallel and competitive assembly: “Multipath agents” show that parameter-efficient adapters and soft routers, only training 0.1% of parameters per integration, deliver test accuracy gains of +0.5% vs. singlepath and up to +1.9% F1 over dense MoE in ensemble sentiment analysis (MoMoE) (Gesmundo, 2023, Shu et al., 17 Nov 2025).
Robustness and critical-thinking support: Modular frameworks enabling user-chosen expert participation, threaded interaction, and mind-map visualization significantly increase both the frequency and depth of critical-thinking behavior relative to group chat baselines (Clarity gain $\Delta=0.87$ vs. $0.39$, $p = .039$ ; interdisciplinary reply rate 45.1% vs. 0%) (Liu et al., 24 Sep 2025).
Token economy and communication overhead: Scaling agent count grows context and token usage superlinearly, with diminishing PoT (performance-per-token) returns beyond 6–10 agents (Xu et al., 12 May 2025). Recommendations include passing compressed summaries and selective routing to mitigate quadratic or linear context growth.

6. Limitations, Trade-Offs, and Design Guidelines

Reported limitations of modular/multi-expert agent frameworks include:

Loss of explicit user control: Abstracted orchestration reduces cognitive load and increases usability but may limit the user’s capacity for agent choice, potentially harming flexibility (Clarke et al., 13 Jan 2024).
Token and memory overhead: As the number of experts increases, per-query context grows superlinearly unless mitigated by dynamic pruning, tree or peer communication, or session variable summarization (Yu et al., 22 Dec 2024, Xu et al., 12 May 2025).
Router and mixture complexity: Linear or shallow routers may be insufficient for deeply non-linear or highly entangled task-expert relationships; learned multi-layer routers or meta-routers are proposed as future work (Gesmundo, 2023, Xu et al., 3 Oct 2025).
Hyperparameter and modular granularity tuning: The effectiveness of division into modules (number, granularity of experts, regularization strengths) is sensitive to application specifics; cross-validation and ablation studies are essential (Antotsiou et al., 2022, Xu et al., 12 May 2025).
Black-box agent adaptation: Some protocols assume agents are immutable black-boxes, which simplifies addition/removal but may preclude tight per-agent adaptation or introspection (though ensemble re-ranking and black-box RAG can partially fill this gap) (Clarke et al., 13 Jan 2024, Puerto et al., 2021).

Recommended best practices include stateless, model-free orchestration (Clarke et al., 13 Jan 2024), module freezing and adapter-based extension (Gesmundo, 2023), session variable storage to reduce context (Yu et al., 22 Dec 2024), and context-mutation for drift correction or behavioral alignment (Sorstkins et al., 18 Sep 2025).

7. Extensions, Generalization, and Theoretical Implications

Modular, multi-expert agent frameworks generalize naturally across tasks, modalities, and learning paradigms:

Domain and modality adaptation: Architectural principles extend to modular molecular reasoning (IR/NMR/MS expert substitution (Noh et al., 22 Aug 2025)), multimodal sentiment analysis pipelines (MoMoE (Shu et al., 17 Nov 2025)), and multi-objective robotics (Kuzmenko et al., 2 Jul 2025, Xu et al., 3 Oct 2025).
Meta-cognitive and reflective extensions: Higher-level “meta” modules for monitoring, reporting, and prompt adaptation enable emergent traits such as self-awareness, reflective learning, and meta-reasoning (Maruyama et al., 26 Aug 2025).
Theory of mind and inference-preserving communication: Modular logic and conservative mapping between expert calculi guarantee sound inference transfer across heterogeneous modules, supporting cognitive realism and robust system coherence (Agustí-Cullell et al., 2013).
Participatory hybrid human–AI systems: Mashups via threaded persona selection, mind map deliberation, and user-engaged agent orchestration increase critical thinking and interdisciplinary discovery, providing an actionable blueprint for open-ended research ideation support (Liu et al., 24 Sep 2025).
Communication protocol scalability: Research highlights critical bottlenecks and proposes that tree-structured or sparse peer-to-peer message passing may cap token overhead at $O(\log n)$ , necessary for very large ensembles (Xu et al., 12 May 2025).

A plausible implication, based on the confluence of modularity, specialization, and adaptive orchestration, is that systems decomposed into sufficiently granular, well-communicating modules will demonstrate both improved learning dynamics and emergent capabilities—such as robust intention, interpretability, and open-ended problem solving—not feasible in monolithic or static architectures.

References:

(Clarke et al., 13 Jan 2024) Clarke et al., "One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI"
(0902.2751) Mirbakhsh et al., "Object Classification by means of Multi-Feature Concept Learning in a Multi Expert-Agent System"
(Puerto et al., 2021) Geigle et al., "MetaQA: Combining Expert Agents for Multi-Skill Question Answering"
(Gesmundo, 2023) D'Ascoli et al., "Multipath agents for modular multitask ML systems"
(Shu et al., 17 Nov 2025) Zhang et al., "MoMoE: A Mixture of Expert Agent Model for Financial Sentiment Analysis"
(Maruyama et al., 26 Aug 2025) Kumar et al., "A Concurrent Modular Agent: Framework for Autonomous LLM Agents"
(Sorstkins et al., 18 Sep 2025) Blanchard et al., "Diagnostics of cognitive failures in multi-agent expert systems using dynamic evaluation protocols and subsequent mutation of the processing context"
(Yu et al., 22 Dec 2024) Li et al., "Modular Conversational Agents for Surveys and Interviews"
(Antotsiou et al., 2022) Isele & Kuhlmann, "Modular Adaptive Policy Selection for Multi-Task Imitation Learning through Task Division"
(Agustí-Cullell et al., 2013) Esteva et al., "Combining Multiple-Valued Logics in Modular Expert Systems"
(Kuzmenko et al., 2 Jul 2025) Rao et al., "MoIRA: Modular Instruction Routing Architecture for Multi-Task Robotics"
(Pei et al., 2019) Lin et al., "A Modular Task-oriented Dialogue System Using a Neural Mixture-of-Experts"
(Xu et al., 3 Oct 2025) Wang et al., "MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning"
(Xu et al., 12 May 2025) Zhou et al., "Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study"
(Noh et al., 22 Aug 2025) Lee et al., "IR-Agent: Expert-Inspired LLM Agents for Structure Elucidation from Infrared Spectra"
(Liu et al., 24 Sep 2025) Huang et al., "Perspectra: Choosing Your Experts Enhances Critical Thinking in Multi-Agent Research Ideation"
(Zhang et al., 4 Jul 2024) Wang et al., "MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices"