Federation of Agents (FoA)

FoA is a distributed orchestration framework that transforms static multi-agent coordination into dynamic, capability-driven collaboration, positioning itself as a challenge to the current single-large-model-dominated paradigm. According to the original forum post, it is presented as a CERN-led effort advancing AI from passive tools toward active "agents."

Core Mechanisms

Versioned Capability Vectors (VCVs)

• Machine-readable agent profiles, searchable via semantic embeddings
• Six components: dense capability embeddings, discrete skill representations, resource requirement vectors, policy compliance flags, canonical embeddings, and a version counter

Semantic Routing

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

Collaborative Refinement

• Agents are grouped into temporary clusters
• Multiple rounds of iterative refinement improve solution quality

FoA vs. Federated Learning

| FoA | Federated Learning | |---|---| | Focuses on inference-time agent orchestration | Focuses on training-time distributed model training | | Emphasizes dynamic collaboration and capability matching | CERN's CAFEIN platform targets federated learning and is a separate project from FoA |

Technical Implementation

• MQTT-based publish-subscribe semantics for scalable messaging
• Hierarchical HNSW indexing 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