Federation of Agents (FoA)

FoA is a distributed orchestration framework that shifts AI from passive tools toward active "agents", replacing static multi-agent coordination with dynamic, capability-driven collaboration.

Core Mechanisms

Versioned Capability Vectors (VCVs)

• Machine-readable agent profiles
• Six components: dense capability embedding, discrete skill representation, resource requirement vector, policy compliance flags, specification embedding, and a version counter
• Searchable via semantic embeddings

Semantic Routing

• Semantic matching using a sharded HNSW index
• Accounts for cost, budget, and policy constraints
• Selects the most suitable combination of agents

Collaborative Refinement

• Agents are grouped into temporary clusters
• Multi-round iterative refinement
• Improves solution quality

FoA vs. Federated Learning

| FoA | Federated Learning | |---|---| | Focuses on inference-time agent orchestration, not model training | Focuses on training-time distributed model training | | Emphasizes dynamic collaboration and capability matching | CERN's CAFEIN platform focuses on federated learning and is a separate project from FoA |

Technical Implementation

• Scalable messaging based on MQTT publish-subscribe semantics
• Hierarchical HNSW index for efficient semantic search
• Six-stage execution pipeline:
1. Task decomposition 2. Agent matching 3. Draft generation 4. Collaborative refinement 5. Result synthesis 6. Final delivery



Experimental Results

• On the HealthBench Hard benchmark:
• 13x improvement over the best single-model baseline
• 6.5x improvement over uncoordinated ensembles
• The system scales horizontally while maintaining consistent performance
• Cluster-enhanced collaboration is especially effective for complex reasoning tasks that require multiple perspectives