Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI

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 architecture combines 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.

The landscape of artificial intelligence has evolved from single AI models to networks of specialized agents that plan, coordinate, and act over extended horizons [1, 2, 3]. This shift toward agentic AI systems represents a fundamental change in how we approach complex problem-solving with AI: rather than relying on a single model to handle all aspects of a task, we now orchestrate collections of specialized agents that can decompose tasks, maintain persistent context, and coordinate their efforts through structured communication towards a common goal. However, current agentic AI systems mainly rely on manually curated integrations and topic-based routing [4, 5], posing constraints on scalability as the heterogeneity of agents grows, and coordination complexity increases, limiting scalability and not addressing the fundamental operational question: who can do what, at what cost, and under which policy constraints?; preventing the realization of the "Internet of Agents" vision [6]. To address this, we introduce Federation of Agents (FoA), a semantics-aware communication fabric that transforms agent coordination from static, topic-based routing to dynamic, capability-driven orchestration. At its core, FoA enables agents, tools, and data stores to advertise Versioned Capability Vectors (VCVs): machine-readable profiles that capture functional capabilities, performance characteristics, operational constraints, and security labels in a structured format.

Problem Statement and Challenges. Despite rapid progress, agentic AI systems still lack principled, searchable capability profiles, making capability discovery and partner selection ad hoc even in prominent orchestration frameworks [4, 7, 6]. Dynamic orchestration remains partially solved: rolebased systems improve structure yet often rely on manual wiring and do not couple decomposition with operational budgets [8, 9]. Meeting resource constraints (latency, bandwidth, energy) alongside semantic fit is remarkable at the edge, where agents run on IoT devices [10], motivating embeddingbased semantic routing and efficient, reliable transports [11, 12, 13, 14]. Heterogeneous security and regulatory regimes further raise policy compliance requirements, demanding auditable enforcement across agents and data boundaries [15]. At scale, coordination overhead and loss of coherence emerge as agent counts and workflow depth grow, calling for sublinear retrieval and structure-aware coordination [16, 17, 18]. Operational observability and reliability are also underdeveloped: real deployments report behavioural variability, drift, and governance gaps in agentic processes and ecosystems [19, 20]. Finally, there are no interoperable protocols and ontologies to standardize capabilities and interactions, limiting portability across stacks and domains [21, 22, 23, 24].

Our Approach. FoA replaces static, topic-centric wiring with dynamic, capability-driven orchestration, aligning with calls for semantics-first coordination in agent ecosystems [21, 22, 4, 7]. Agents publish (VCVs), structured, versioned profiles embedded in a high-dimensional space, so capabilities become searchable artifacts compatible with emerging interoperability efforts (e.g., Model Context Protocol (MCP)-based capability schemas) [23, 24]. We index VCVs using a sharded Hierarchical navigable small world (HNSW) index to support sublinear matching at scale while preserving nuanced distinctions among related skills [18]. At dispatch time, FoA applies semantic routing that couples profiles’ similarities with policy checks and resource budgets (i.e., latency, bandwidth, energy consumption), rather than relying on keywords or static registries [11, 15]. Operational feasibility is enforced with transport-aware choices for IoT settings, where the Message Queuing Telemetry Transport (MQTT) protocol provides efficient, reliable delivery under constrained networks [12, 13, 14]. For dynamic task decomposition, FoA elicits candidate breakdowns from compatible agents and merges them into a consensual directed acyclic graph DAG, drawing on role-structured collaboration patterns from multi-agent systems [8, 9]. Finally, intelligent orchestration optimizes assignments over semantic fit and operational cost, supporting centralized and decentralized modes [16, 17]; this aligns with distributed orchestration needs observed in infrastructures such as CAFEIN®, CERN’s federated AI platform, where privacy-preserving data access and cross-institution coordination are central constraints [25, 26].