Agentic Multi-Agent Systems

• Agentic multi-agent systems are distributed AI frameworks consisting of autonomous agents, often powered by LLMs, that dynamically coordinate to solve complex, multi-step tasks.
• They leverage probabilistic architectural paradigms and adaptive workflow sampling to optimize resource usage, inter-agent collaboration, and dynamic reasoning.
• Enhanced with robust security measures, formal verification protocols, and collaborative learning, these systems achieve efficient task delegation and resilient performance.

Agentic multi-agent systems are distributed artificial intelligence systems in which collections of autonomous “agents,” typically empowered by LLMs, collaborate through structured workflows and adaptive coordination mechanisms to solve complex, multi-step tasks that exceed the capacity of single agents. In contrast to both classical static multi-agent architectures and basic distributed systems, agentic multi-agent systems emphasize dynamic, goal-directed orchestration, emergent behavior, resource-aware adaptation, and robust delegation—frequently leveraging recent advances in LLMs, probabilistic architecture sampling, and integrated reasoning and tool-use workflows.

1. Foundations and Conceptual Distinctions

Agentic multi-agent systems inherit core properties from the longstanding field of intelligent agents and multi-agent systems: autonomy, proactivity, reactivity, and social (collaborative) capability (Botti, 2 Jun 2025). Classic formalisms, such as the Belief-Desire-Intention (BDI) model, have traditionally structured agent reasoning:

$\text{Agent} \equiv \{B, D, I\}$

where $B$ denotes beliefs, $D$ desires, and $I$ intentions.

The agentic paradigm explains how LLM-powered agents extend these qualities by providing persistent long-term memory, dynamic context retention, planning modules (e.g., chain-of-thought), and advanced inter-agent communication and tool-use layers (Raza et al., 4 Jun 2025). Such systems are distinguished from both monolithic neural pipelines and rigid rule-based agent collectives by their architectural flexibility, memory persistence, dynamic reasoning, and self-adaptive workflows.

Recent literature critiques the indiscriminate use of terms such as “agentic AI” or “multiagentic AI,” faulting the conflation of legacy agent concepts with LLM-empowered systems, and insists on building upon established terminologies and coordination protocols for conceptual clarity and research rigor (Botti, 2 Jun 2025).

2. Probabilistic Architectural Paradigms and Dynamic Workflow Sampling

A salient development is the agentic supernet paradigm (Zhang et al., 6 Feb 2025), which departs from static, one-size-fits-all multi-agent architectures. The agentic supernet is a continuous, probabilistic distribution over composable multi-agent workflows:

$A = \{T, O\} = \{T_\ell(O)\}_{\ell=1}^L$

where each layer $\ell$ in the workflow has a parameterized probability distribution $T_\ell(O)$ over candidate operator modules ($O$), such as Chain-of-Thought (CoT), self-consistency, or multi-agent debate.

Dynamic inference uses a controller network $Q_\phi$ to sample query-dependent architectures $G$ from the supernet for each input, enabling the automatic tailoring of resource usage and reasoning complexity: $$p(a \mid q, T, O) = \int e(a \mid g) Q_\phi(G \mid q, T, O)dg$$ with an “early-exit” operator $O_{\text{exit}}$ to optimize efficiency for simpler queries.

Optimization of supernet parameters incorporates environmental rewards and cost terms, balancing expected utility and resource expense: $$\max \mathbb{E}_{(q,a)\sim D} [ U(G; q, a) - \lambda \cdot C(G; q) ]\,, \quad G \in A$$

Empirical evaluations on math reasoning (GSM8K, MATH), code generation (HumanEval, MBPP), and tool use (GAIA) demonstrate that MaAS achieves 0.54%–11.82% higher accuracy and consumes as little as 6–45% of the inference cost relative to baseline agentic systems, with robust transfer across datasets and LLM backbones (e.g., GPT-4-mini, Qwen-2.5-72b, llama-3.1-70b).

3. Semantic Coordination, Optimization, and Cross-Layer Conflict Resolution

Agentic multi-agent frameworks such as SANNet (Xiao et al., 25 May 2025) introduce semantic-aware orchestration wherein user-level goals are inferred via natural language processing and decomposed across specialized agent layers (application, network, physical). The agent controller matches subtasks with “agent cards” that encode agent capabilities and constraints.

A principal challenge addressed is inter-agent conflict in multi-objective optimization. SANNet’s dynamic weighting mechanism optimizes a vector-valued loss:

$$\mathbf{L}_m(\mathcal{W}_m) = \langle l_m^a(\mathcal{W}_m, \omega^a), l_m^p(\mathcal{W}_m, \omega^p), l_m^n(\mathcal{W}_m, \omega^n) \rangle$$

where weights $\gamma_i$ are adaptively tuned to balance gradient contributions across agents. Theoretical guarantees (Theorem 1 in (Xiao et al., 25 May 2025)) ensure convergence of the conflict error $E_C$, and hardware prototyping demonstrates a 63% reduction in conflict error over static approaches.

4. Collaborative Learning, Emergent Curriculum, and Knowledge Sharing

Agentic multi-agent systems are increasingly deployed for collaborative and lifelong learning. In MOSAIC (Nath et al., 5 Jun 2025), agents maintain modular neural policies, selectively share binary masks representing reusable skills, and perform similarity-driven knowledge transfer grounded in Wasserstein task embeddings:

$$d_{\text{cos}}(v_i, v_j) = \frac{ \langle \vec{v}_i, \vec{v}_j \rangle }{ \| \vec{v}_i \| \cdot \| \vec{v}_j \| }$$

Together with asynchronous communication, this arrangement lets agents independently discover, request, and assimilate useful policy fragments from peers, resulting in improved sample efficiency and the emergence of task curricula—easy tasks solved first provide scaffolding for more difficult ones.

Empirical results demonstrate that such collaborative learning yields 2.7× faster convergence and higher final returns than non-communicating baselines on reinforcement learning benchmarks (CT-Graph, MiniHack, MiniGrid).

5. Security, Trust, and Formal Verification Properties

Agentic multi-agent systems are exposed to novel forms of risk, including autonomy abuse, persistent memory contamination, coordination failures, and misuse of external tools (Raza et al., 4 Jun 2025, Gosmar et al., 18 Sep 2025). Frameworks such as TRiSM for Agentic AI (Raza et al., 4 Jun 2025) adapt enterprise security pillars—governance, explainability, ModelOps, and privacy/security—to the agentic paradigm.

New system metrics, including the Component Synergy Score (CSS) for collaborative effectiveness and Tool Utilization Efficacy (TUE) for API/tool use precision, enable performance and vulnerability auditing.

Formal modeling efforts (Allegrini et al., 15 Oct 2025) establish unified semantic frameworks comprising host agent models and task lifecycle models, each annotated with temporal logic properties (liveness, safety, completeness, fairness), enabling model checking and automated detection of coordination errors, deadlocks, and privilege escalations: $$\text{Example: } \quad \mathrm{AG}(\text{Req}_U \rightarrow \mathrm{AF} \text{Resp}_H)$$ Inter-agent protocols (e.g., MCP for tool calls, A2A for agent collaboration) are treated as first-class components in these verification efforts.

6. Benchmarking, Emergent Scalability, and Environmental Robustness

Scaled evaluation environments such as CREW-Wildfire (Hyun et al., 7 Jul 2025) stress-test agentic multi-agent systems in dynamic, stochastic, partially observable scenarios with heterogeneous agent types (firefighters, drones, helicopters, bulldozers). Realistic, procedurally generated wildfire scenarios demand adaptive planning, robust coordination, and information sharing across long horizons and large agent populations.

Empirical comparisons reveal that, despite recent advances, state-of-the-art agentic frameworks continue to face significant gaps in large-scale synchronization, plan adaptation, spatial reasoning, and robust task allocation under uncertainty. Metrics such as Behavior Competency Scores (BCS) help quantify system capabilities across observation sharing, plan adaptation, and objective prioritization.

7. Communication Protocols and Architectural Serviceability

Modern agentic systems require robust, interoperable communication protocols supporting dynamic capability discovery, task negotiation, and secure delegation. Advances such as the Agent-to-Agent (A2A) protocol for standardized agent interactions (Duan et al., 17 Aug 2025), together with protocol meta-coordination layers (e.g., Agora), address interoperability and life cycle management challenges. At the infrastructure layer, A2A supports multiple agent discovery and session management modes, but exposes scalability and resource-awareness limitations when deployed at the network edge.

Research proposes the integration of gossip protocols (Habiba et al., 3 Aug 2025) as a redundancy-oriented, eventually consistent “substrate” for emergent context dissemination, supplementing deterministic, structured protocols. This overlay fosters swarm intelligence, adaptive self-organization, and resilience at scale, while raising new questions about semantic filtering, trust calibration, and knowledge decay.

Concluding Perspective

Agentic multi-agent systems are characterized by adaptive, sample-efficient collaboration, context-sensitive workflow sampling, robust semantic coordination, and integrated risk management. Critical advances include continuous architecture distributions (agentic supernet), conflict-resolving optimization, modular collaborative learning, and verification-aware formal modeling. As system-scale, task complexity, and deployment constraints (e.g., at the edge) grow, pressing research challenges remain in scalability, semantic protocol interoperability, continuous performance benchmarking, and end-to-end safety. The field is rapidly converging toward unified frameworks that combine classical agent theory, LLM-driven reasoning, probabilistic workflow search, and lifecycle-aware service design, setting the direction for autonomous, efficient, and accountable intelligent agents in computational societies.