Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI (2509.20175v1)

Abstract: We present Federation of Agents (FoA), a distributed orchestration framework that transforms static multi-agent coordination into dynamic, capability-driven collaboration. FoA introduces Versioned Capability Vectors (VCVs): machine-readable profiles that make agent capabilities searchable through semantic embeddings, enabling agents to advertise their capabilities, cost, and limitations. Our aarchitecturecombines three key innovations: (1) semantic routing that matches tasks to agents over sharded HNSW indices while enforcing operational constraints through cost-biased optimization, (2) dynamic task decomposition where compatible agents collaboratively break down complex tasks into DAGs of subtasks through consensus-based merging, and (3) smart clustering that groups agents working on similar subtasks into collaborative channels for k-round refinement before synthesis. Built on top of MQTT,s publish-subscribe semantics for scalable message passing, FoA achieves sub-linear complexity through hierarchical capability matching and efficient index maintenance. Evaluation on HealthBench shows 13x improvements over single-model baselines, with clustering-enhanced laboration particularly effective for complex reasoning tasks requiring multiple perspectives. The system scales horizontally while maintaining consistent performance, demonstrating that semantic orchestration with structured collaboration can unlock the collective intelligence of heterogeneous federations of AI agents.

• The paper presents a distributed framework that leverages Versioned Capability Vectors to enable dynamic task assignment and searchable agent discovery.
• It employs semantic routing, smart clustering, and iterative consensus to achieve significant performance gains, including a 13x improvement on a healthcare benchmark.
• The methodology integrates MQTT-based communication and GRPO-aligned agent roles to optimize latency, resource usage, and policy compliance in heterogeneous environments.

Federation of Agents: Semantics-Aware Orchestration for Large-Scale Agentic AI

Introduction and Motivation

The "Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI" (FoA) presents a distributed orchestration framework that advances agentic AI from static, topic-based coordination to dynamic, capability-driven collaboration. The core innovation is the introduction of Versioned Capability Vectors (VCVs), which encode agent capabilities, operational constraints, and policy compliance in a machine-readable, semantically embedded format. This enables scalable, searchable capability discovery and assignment, addressing the limitations of manual wiring and static role-based routing in existing multi-agent systems.

The framework is motivated by the increasing complexity and heterogeneity of agentic AI ecosystems, where the operational question of "who can do what, at what cost, and under which policy constraints?" becomes central. FoA leverages semantic routing, dynamic task decomposition, and smart clustering to orchestrate federations of specialized agents, supporting both centralized and decentralized execution modes. The system is built atop MQTT's publish-subscribe semantics, providing scalable, reliable message passing suitable for resource-constrained and heterogeneous environments.

Figure 1: Federation of Agents architecture, showing VCV advertisement, semantic routing, MQTT-based communication, and DAG-based synthesis of subtask outputs.

System Architecture and Core Artifacts

Versioned Capability Vectors (VCVs)

Each agent in FoA advertises a VCV, a structured tuple comprising:

• $\mathbf{c}_{a_i}$: Dense capability embedding (semantic representation of skills)
• $\mathbf{s}_{a_i}$: Bloom filter over discrete skills
• $\mathbf{r}_{a_i}$: Resource requirements and QoS guarantees
• $\mathbf{p}_{a_i}$: Policy compliance flags
• $\mathbf{e}_{a_i}$: Spec embedding (behavioral constraints)
• $v_{a_i}$: Version counter

VCVs are indexed using sharded HNSW graphs for sublinear retrieval, enabling efficient matching of tasks to agents based on semantic similarity, resource fit, and policy compliance. The embedding pipeline utilizes specialized tokenization and sentence embedding models (e.g., Nomic Embed, EmbeddingGemma), with L2 normalization for cosine similarity computation.

Orchestrator and Agent Roles

• Orchestrator (Agent-0): Maintains the VCV index, performs dynamic task decomposition, forms clusters, and orchestrates collaboration. Implements a $\Delta$-gossip protocol for VCV updates and manages DAG-based execution order.
• Agents (Agent-1): Specialized LLM-based agents aligned via GRPO, equipped with tool-use controllers and local resource access. Agents generate initial drafts, participate in cluster-based refinement, and adhere to behavioral Specs embedded in their VCVs.

Dynamic Task Decomposition and Assignment

Upon receiving a complex task, the orchestrator embeds the task description, queries the VCV index for compatible agents, and solicits candidate decompositions. These are merged into a consensus DAG, with subtasks assigned to agents via a constrained optimization over semantic similarity, policy compliance, resource fit, and spec alignment. The assignment matrix $\mathbf{X}$ is computed to maximize expected utility under capacity constraints.

Figure 2: Orchestrator-driven sub-task decomposition, semantic embedding, similarity matrix computation, and assignment to agents/clusters.

Smart Clustering and Collaborative Refinement

Agents assigned to the same subtask are grouped into clusters based on hierarchical similarity across capability, resource, draft quality, and spec embeddings. Within clusters, agents exchange drafts and critiques for $k$ refinement rounds, employing majority or reputation-weighted voting to reach consensus. This collaborative protocol enhances solution quality, particularly for tasks requiring multiple perspectives.

Figure 3: Collaborative refinement within high-similarity clusters, with $k$ rounds of draft exchange and consensus emission.

Execution Flow and Algorithmic Details

The FoA execution pipeline comprises six phases:

1. Dynamic Task Decomposition: Task embedding, agent scoring, candidate proposal merging, and DAG validation.
2. First Draft Generation: Agents retrieve context, generate initial answers conditioned on Specs and resources.
3. Cluster Formation: Hierarchical clustering of agents based on multi-dimensional similarity.
4. Intra-Cluster Consensus: Iterative refinement and voting within clusters, bounded by $k$ rounds.
5. Reporting: Emission of TASK_COMPLETE signals and DAG state updates.
6. Result Synthesis: Topological traversal of the DAG, merging predecessor outputs via meta-prompting and chain-of-thought steering.

The computational complexity is dominated by similarity computations ($O(n d)$), proposal merging ($O(m |\mathcal{S}| \log |\mathcal{S}|)$), and intra-cluster communication ($O(k |C|^2)$), with practical scalability ensured by parallelization and cluster size capping.

Experimental Evaluation

FoA is evaluated on the HealthBench Hard benchmark, comprising 1,000 multi-turn healthcare conversations with rigorous physician-validated rubrics. The framework achieves an overall score of $0.13$, representing a 13x improvement over the best single-agent baseline (Medgemma) and a 6.5x improvement over uncoordinated ensembles. Capability-aware routing and collaborative clustering yield consistent gains across all evaluation axes, with the most pronounced benefits in high-stakes, context-sensitive reasoning tasks.

Implementation Considerations

• MQTT Transport: Structured topic hierarchy for job submission, capability updates, cluster communication, policy enforcement, and result synthesis. MQTTv5 provides QoS guarantees, retained messages, and efficient asynchronous communication.
• Embedding Pipeline: Two-stage embedding with dimensionality reduction for storage efficiency; cosine similarity for routing and clustering.
• Agent Alignment: GRPO-based fine-tuning on domain data, with Specs serving as reward functions for behavioral alignment.
• Resource Constraints: Agents optimize for latency, energy, and memory, supporting deployment on consumer-grade hardware (≤20B parameter LLMs).
• Security and Policy Enforcement: Bloom filters for unsafe content, policy flags in VCVs, and auditability via provenance metadata.

Limitations and Future Directions

FoA's effectiveness is bounded by embedding quality and cold-start issues for novel capabilities. Clustering may be sub-optimal in highly heterogeneous domains, and VCVs may not capture emergent or compositional skills. Communication overhead within clusters limits practical size, and adversarial misrepresentation of capabilities remains an open challenge. Future work includes RL-based adaptive routing, cross-cluster knowledge sharing, zero-knowledge capability attestations, and integration with trusted execution environments.

Theoretical and Practical Implications

FoA establishes a principled substrate for capability-driven orchestration, enabling scalable, auditable, and policy-compliant multi-agent collaboration. The framework's semantic routing and smart clustering mechanisms provide a foundation for the "Internet of Agents" vision, supporting horizontal scalability and robust coordination in heterogeneous environments. The demonstrated performance gains on HealthBench validate the practical utility of structured collaboration and semantic orchestration.

Conclusion

Federation of Agents introduces a semantics-aware communication fabric that transforms agentic AI coordination through searchable capability profiles, dynamic decomposition, and collaborative refinement. The system achieves significant improvements in solution quality and scalability, addressing core challenges in multi-agent orchestration, semantic routing, and trust management. FoA provides a robust foundation for future research and deployment of large-scale, collaborative AI ecosystems, with strong implications for both theoretical advancement and real-world impact.