Skip to main content

Compound AI

Compound AI Systems

The key to success lies in finding the right balance between component optimization and system-level performance, while maintaining flexibility to incorporate new advances in the field.

Definition​

This framework promises to enable building sophisticated AI agents that can:

  • Handle multiple modalities
  • Optimize performance across quality, latency, and cost
  • Scale efficiently through distributed processing
  • Integrate with external systems and knowledge bases

Foundation Models & Infrastructures​

  • Compound AI represents a system where multiple specialized models work together across modalities (text, audio, vision) along with APIs, storage systems, and knowledge bases to deliver optimal results
  • The transition from single models to distributed systems enables more accurate and specialized task handling through expert models for narrow tasks

Architectural Components​

Distributed Inference Engine that:

  • Splits models into pieces for efficient scaling
  • Operates across multiple regions (North America, EMEA, Asia)
  • Handles global load balancing
  • Matches hardware to specific workload types

Design Principles​

Smart Agent Framework Design Principles

Modular Architecture: Build specialized components for different modalities:

  • Text processing
  • Audio processing
  • Vision processing
  • Embedding generation
  • Knowledge storage and retrieval

Optimization Layer: Implement a three-dimensional optimization across:

  • Quality
  • Latency
  • Cost

Integration Layer: Connect with:

  • Vector databases for knowledge storage
  • External APIs for real-time data
  • Internal proprietary systems
  • Storage and database systems[1]

Implementation​

Core Infrastructure

class AgentFramework:
    def __init__(self):
        self.models = {
            'text': TextProcessor(),
            'audio': AudioProcessor(),
            'vision': VisionProcessor(),
            'embedding': EmbeddingGenerator()
        }
        self.optimizer = QualityLatencyCostOptimizer()
        self.knowledge_base = VectorStore()

Task Orchestration

class TaskOrchestrator:
    def decompose_task(self, task):
        # Break complex tasks into specialized subtasks
        subtasks = []
        return subtasks

    def route_to_expert(self, subtask):
        # Match subtask to appropriate expert model
        return expert_model

Providers​