Multi-Agent Architectures

Updated 16 December 2025

Multi-Agent Architectures are a system design paradigm where multiple autonomous agents interact and collaborate to solve tasks that a single agent cannot efficiently handle.
These architectures employ diverse coordination mechanisms—hierarchical, decentralized, hybrid, and publish–subscribe—to optimize task allocation, resource efficiency, and system robustness.
Advancements in modularity, real-time collaboration, and adaptive protocols have expanded their application in robotics, drug discovery, software engineering, and regulatory technology.

A multi-agent architecture is a system design paradigm in which a set of autonomous, interacting software or physical agents collaboratively accomplish a collection of tasks that may be impossible, inefficient, or brittle for a single agent. These architectures span a rich design space, balancing centralized coordination against emergent decentralization, and are increasingly used in domains ranging from software engineering and industrial control to robotics, regulatory technology, and drug discovery. Recent advances have driven multi-agent architectures toward modularity, adaptability, and robust real-time collaboration in dynamic, unpredictable environments.

1. Foundational Principles and Architectural Taxonomies

Two principal axes define the space of multi-agent architectures: organizational hierarchy and collaboration style. Four canonical archetypes emerge (Masterman et al., 17 Apr 2024):

Hierarchical (Leader–Follower): A central agent coordinates a team of workers, dictating subtasks and acting as a planning bottleneck. Communication predominantly flows between the leader and individual followers.
Decentralized (Fully Collaborative): All agents are peers, leveraging shared blackboards or group chats, with task assignment and solution synthesis emerging through negotiation or consensus.
Hybrid (Dynamic/Rotating Leadership): Teams form or restructure per task/phase, with leadership changing over time based on metrics such as contribution, maximizing resilience and adaptability.
Publish–Subscribe (Structured Collaboration): Agents publish or subscribe to artifacts and only act upon specific message classes, controlling communication overhead and reducing scope drift.

A comprehensive taxonomy for modern LLM-powered architectures classifies systems along orthogonal axes: levels of agent autonomy (static, adaptive, self-organizing), alignment with human oversight (integrated, user-guided, real-time responsive), and architectural viewpoints (goal/task management, agent composition, collaboration protocols, context interaction) (Händler, 2023). This scheme produces a 3×3×4 matrix of potential design patterns and reveals that state-of-the-art systems rarely employ the highest levels of autonomy or real-time alignment, highlighting open challenges in reliability and robustness.

2. Core Modular Structures and Patterns

Modern multi-agent architectures employ explicit modularity, often separating global coordination and local execution. Notable patterns include:

Dual-Plane Separation: DRAMA exemplifies this via a control plane (monitoring, planning, event detection, state DB) and a worker plane (autonomous, heterogeneous agents executing assigned tasks) (Wang et al., 6 Aug 2025). A lightweight message-driven API separates "who does what" from "how to do it," supporting both flexibility and strong division of responsibilities.
Core-Agent Modularization: The LLM-Agent-UMF framework formalizes Core-Agents as five-tuples (Planning, Memory, Profile, Action, Security), with clear instantiation patterns for uniform, hybrid, and heterogeneous agent configurations (Hassouna et al., 17 Sep 2024).
Layered Role and Scene Decomposition: The Athenian Academy model partitions systems into seven layers, from multi-agent collaboration down to model fusion and agent synthesis, to support cross-domain adaptability, capability modularity, and efficient collaboration in complex environments like AI art creation (Zhai et al., 17 Apr 2025).
Task-Pipeline Architectures: In MAAD for software design (Li et al., 28 Jul 2025, Zhang et al., 26 Mar 2025) and MADD for drug discovery (Solovev et al., 11 Nov 2025), agent workflows instantiate Analyst/Decomposer, Modeler/Orchestrator, Designer/Summarizer, and Evaluator/Chat agents with domain-specific interfaces, artifact stores, and protocol-driven coordination.

Generic interaction protocols include publish–subscribe, direct point-to-point, synchronous/asynchronous rounds, and task blackboards (Masterman et al., 17 Apr 2024). Many patterns employ feedback loops—either behavioral (reflective evaluation and planning adjustment) or executional (dynamic task handover, resilience mechanisms in case of agent dropout).

3. Task Allocation, Adaptation, and Resource Efficiency

Task allocation strategies are central to multi-agent architectures and have evolved from static, rule-based mappings to dynamic, context-sensitive, and learning-enabled protocols:

Affinity-Based Scheduling: DRAMA's scheduler computes per-agent/per-task affinity scores considering capacity match, distance, and current load, optimizing a constrained objective under dynamic agent/task lifecycles (Wang et al., 6 Aug 2025).
Router-Based Expert Selection: In foundation-design automation, systems use LLM-embedding-driven classifiers followed by expert-selection modules for directed dispatch to task-specialist agents, achieving significant accuracy improvements (up to 43.75 percentage points over designer-checker workflows) (Youwai et al., 13 Jun 2025).
Neural Orchestration: MetaOrch learns to map preprocessed task/agent vectors and performance history to agent selection distributions, optimizing over interpretable axes (completeness, relevance, confidence) using fuzzy evaluation for improved adaptability and extensibility (Agrawal et al., 3 May 2025).
Automated Architecture Search (Agentic Supernet): MaAS treats the space of possible agentic workflows as a stochastic supernet sampled per query, optimizing for both accuracy and inference cost via a distributional controller and query-aware early-exit, reducing cost by up to 75% while outperforming strong baselines (Zhang et al., 6 Feb 2025).

Dynamic adaptation is universally supported through event-driven rescheduling (e.g., agent join/leave, task anomaly, failure recovery), feedback-informed refinement, and in some architectures, explicit learning-based reconfiguration (RL/GRPO in robotics and planning (Sun et al., 30 Nov 2025), federated learning in collaborative human-robot settings (Papadopoulos et al., 2020)).

4. Engineering for Robustness, Scalability, and Openness

State-of-the-art systems focus on robustness, scalability, and the capacity to handle partial failures and open-world churn:

Resilience Guarantees: DRAMA ensures that no task is ever lost, with seamless handover and bounded scheduling overhead under agent churn (Wang et al., 6 Aug 2025).
Hierarchical/Decentralized Organization: Hierarchical architectures (e.g., TAG's LevelEnv) abstract each MDP level as the environment for the next, allowing arbitrary depth and heterogeneous RL methods per level. This yields substantial gains in both final performance and learning speed over monolithic or flat MARL baselines (Paolo et al., 21 Feb 2025).
Context Persistence and Standardization: MCP-based frameworks employ structured, versioned context servers for consistent sharing and coordination, reducing communication overhead and more than doubling context recall continuity compared to baseline approaches (Krishnan, 26 Apr 2025).
Formal Self-Adaptivity and Openness: Pattern-based π-ADL architectures (Weyns & Oquendo) support virtual environments and situated agents whose perception, knowledge, action-selection, and communication can adapt dynamically, enabling agents to join/leave at will without fragility (Weyns et al., 2019).
Scalability in Distributed Control: In cyber-physical domains (e.g., data center control (Astudillo et al., 21 Feb 2025), large-scale robotics (2207.13255)), decentralized DDP/ADMM and multi-layer RL achieve high sample efficiency, support thousands of agents/drones, and maintain system-level constraints through rigorous consensus protocols.

5. Domain-Specific Instantiations and Impact

Multi-agent architectures find diverse instantiations and domain impact:

Automated Software Architecture: MAAD enables full pipeline automation from SRS parsing to quality-evaluated UML artifact generation, leveraging knowledge integration from previous designs, literature, and expertise. Feedback loops ensure alignment with functional and non-functional requirements, and LLM selection critically affects performance (Li et al., 28 Jul 2025, Zhang et al., 26 Mar 2025).
Drug Discovery Pipelines: MADD orchestrates agents for de novo compound generation, in silico screening, property filtering, and orchestration, achieving superior hit identification compared to LLM-only approaches via modular “broker” design and large-scale pipeline automation (Solovev et al., 11 Nov 2025).
Regulatory and Safety-Critical MAS: Blockchain-enabled regulatory platforms introduce multi-layered agent, data, and regulatory application layers. Modules for behavior tracing, on-chain arbitration, dynamic reputation, and diffusion-based risk forecasting ensure transparency, accountability, and resilience in safety-critical collaborations (finance, healthcare) (Hu et al., 11 Sep 2025).
Human-Robot Collaboration: InteractGen demonstrates that decomposing monolithic foundation models into perception, planning, assignment, validation, and reflection agents, orchestrated via a shared memory and message pool, yields substantial gains in success rate, robustness, and labor division in embodied service robotics (Sun et al., 30 Nov 2025).

A summary table of selected reference architectures and their distinctive features:

Architecture	Key Property	Domain/Impact
DRAMA	Modular control/worker planes, affinity-based scheduler, seamless handover	Dynamic embodied MAS (Wang et al., 6 Aug 2025)
LLM-Agent-UMF	Core-agent modularization, active/passive/graph taxonomy	Advanced LLM MAS design (Hassouna et al., 17 Sep 2024)
MAAD	LLM-based analyst/modeler/designer/evaluator, knowledge-driven feedback	Software arch. automation (Li et al., 28 Jul 2025, Zhang et al., 26 Mar 2025)
MCP	Standardized context protocol, publish–subscribe, scalable task assignment	Knowledge management, R&D (Krishnan, 26 Apr 2025)
MetaOrch	Supervised neural orchestrator, fuzzy evaluation for agent selection	Heterogeneous MAS (Agrawal et al., 3 May 2025)
MaAS	Agentic supernet, query-aware early-exit, joint cost–performance optimization	Generalized, resource-efficient MAS (Zhang et al., 6 Feb 2025)
TAG	Hierarchical, decentralized RL (LevelEnv)	Deep MARL, control (Paolo et al., 21 Feb 2025)
InteractGen	Role-specialized agent pipeline for perception/planning/etc., embodied deployment	Human–robot collab. (Sun et al., 30 Nov 2025)

6. Design Trade-Offs, Challenges, and Open Research Problems

Significant challenges persist as systems scale and are integrated into more open, non-stationary environments:

Balancing Autonomy and Alignment: Increased agent autonomy (dynamic planning, agent spawning) risks unbounded negotiation or deadlock without accompanying human-in-the-loop or real-time responsive alignment controls (Händler, 2023).
Robustness against Cascading Errors: Prompt-based agent delegation and pure LLM directed collaboration are susceptible to hallucination cascades, non-terminating loops, and alignment drift. Formal communication contracts and runtime verification represent ongoing research needs.
Scalability Limits: Context and coordination explosion, heterogeneous runtime environments, and sub-100ms real-time requirements pose significant practical limits to current protocols (Krishnan, 26 Apr 2025).
Security, Privacy, and Regulation: Open and collaborative MASs must integrate end-to-end cryptographic protections, verifiable logging, and economic incentive mechanisms (e.g., blockchain arbitration, reputation tokenomics) for adversary resistance and compliance (Hu et al., 11 Sep 2025).
Domain Adaptability: Engineering modular yet expressive interfaces and ontologies for cross-domain and cross-modal tasking remains an open area, particularly as system complexity and scale grow.

Emerging research focuses on adaptive communication protocols, formal alignment contracts, real-time interceptors for safe override, and self-organizing teams with fluid specialization and robust emergent behaviors.

7. Quantitative Benchmarks and Empirical Highlights

Recent work demonstrates concrete gains through architectural innovation:

DRAMA reduces resource use by 13–17% and uniquely maintains 100% success rate under agent dropout in dynamic scenarios (Wang et al., 6 Aug 2025).
Router-based MAS for foundation design achieves up to 43.75 pp accuracy gains over conventional workflows (Youwai et al., 13 Jun 2025).
MaAS cuts inference API costs by up to 75% while surpassing strong baselines in code and math reasoning (Zhang et al., 6 Feb 2025).
MCP context protocol lifts task completion rate by 33% and reduces coordination overhead by over 2x compared to traditional approaches (Krishnan, 26 Apr 2025).
InteractGen improves real-world task success rate from 13–51% (state-of-the-art VLM baselines) to 70% in human-robot collaboration tasks (Sun et al., 30 Nov 2025).

Table: Quantitative Empirical Comparisons

System	Metric	Value	Relative Gain	Context
DRAMA	Total steps (TS)	–17%	vs static baselines	Dynamic task allocation
Router MAS	Shallow Fnd. Acc.	95.00%	+8.75 pp vs Grok 3	Civil engineering
MetaOrch	Agent select. acc.	86.3%	+63.3 pp vs random	Heterogeneous MAS
MaAS	API cost (MATH)	$0.42	–75% vs AFlow	Automated arch. search
InteractGen	Success Rate (SR)	70.0%	+19–57 pp vs baselines	Service robotics

Detailed trade-offs, ablation studies, and case-specific metrics are reported in the source literature, with further detail available in the referenced supplementary materials.

References:

(Wang et al., 6 Aug 2025, Li et al., 28 Jul 2025, Zhang et al., 26 Mar 2025, Youwai et al., 13 Jun 2025, Sun et al., 30 Nov 2025, Agrawal et al., 3 May 2025, Zhang et al., 6 Feb 2025, Paolo et al., 21 Feb 2025, Hassouna et al., 17 Sep 2024, Masterman et al., 17 Apr 2024, Krishnan, 26 Apr 2025, Solovev et al., 11 Nov 2025, Hu et al., 11 Sep 2025, 2207.13255, Papadopoulos et al., 2020, Weyns et al., 2019, Zhai et al., 17 Apr 2025, Händler, 2023, Astudillo et al., 21 Feb 2025, Maalal et al., 2012)