From Models to Compound AI Systems: Building the Future of AI

What are compound AI systems?

Unlike traditional AI approaches that rely on one massive model to handle everything, compound AI systems combine large language models with specialized tools, external databases, and domain-specific models to tackle complex tasks more efficiently.

Instead of solely pursuing ever-larger monolithic models, the orchestration of multiple, targeted AI components is emerging as a powerful path. While well-scaled, generalist foundation models have demonstrated superior performance over small, specialized models on many tasks (a trend evidenced by models like DeepMind's Gopher and Chinchilla), they also come with a significant computational and financial cost to train and run. 

Consequently, orchestrating multiple AI components delivers a compelling alternative that can provide superior performance, better cost control, and enhanced transparency by distributing the workload and leveraging specialized expertise. Compound architectures offer the flexibility to optimize individual components independently while maintaining the ability to adapt quickly to changing requirements.

Core principles and design patterns of compounding AI

Compounding AI represents a fundamental transformation in AI system architecture. This paradigm shift represents more than just structural changes—it's a complete reimagining of how we build, deploy, and scale AI solutions.Compound systems outperform their monolithic counterparts by effectively combining diverse model strengths while integrating real-time data streams dynamically.

The modular design philosophy driving this evolution centers on decomposition and specialization. Rather than attempting to solve all problems with a single, massive model, we architect systems as interconnected components, each optimized for specific tasks. This approach mirrors successful software engineering principles: loose coupling, high cohesion, and separation of concerns. By breaking complex AI workflows into discrete, manageable modules, we achieve unprecedented flexibility and maintainability.

Why build compound AI systems?

• Multi-component integration - Seamless orchestration of specialized agents, registries, and planners working in concert

• Specialized task optimization - Individual components fine-tuned for specific computational requirements and domain expertise

• Improved flexibility and performance - Dynamic resource allocation and real-time adaptation to changing workloads

• Enterprise readiness - Robust blueprint architecture supporting scalable deployment across distributed clusters

These systems effectively exemplify these principles. OpenAI's ChatGPT combines retrieval, reasoning, and generation components. At Microsoft Build 2025, the Copilot Studio team unveiled multi‑agent orchestration, a system that allows teams of agents, each with specific roles, to work together under a central coordinating agent. For example, one agent could pull data, another drafted documents, and another scheduled tasks–all in an orchestrated flow.

Compound AI systems' blueprint architecture implements these patterns through distributed components: agent registries manage model inventories, task planners optimize workflow execution, data registries ensure seamless information flow, and optimizers dynamically allocate computational resources. This distributed approach allows scaling specific components based on demand while maintaining system coherence and performance.

Building and testing compound systems: Best practices

When architecting compound AI systems, we face unique challenges that require specialized approaches beyond traditional software development practices.

• Implementation strategy. The first step is to containerize each component using Docker, ensuring consistent environments across development and production. Kubernetes orchestration enables automatic scaling and service discovery, while implementing circuit breakers prevents cascade failures between components.

• Testing multi-component systems. A successful testing strategy employs contract testing to verify API interactions between services, combined with end-to-end integration tests using synthetic data pipelines. This means implementing chaos engineering practices, deliberately introducing failures to validate system resilience.

• Monitoring and observability. The next step is deploying distributed tracing to track requests across components, complemented by centralized logging with ELK stack, for example. Custom metrics monitor AI model performance, data drift, and inference latency through Prometheus and Grafana dashboards.

• Error management. Implement graceful degradation patterns where critical components have fallback mechanisms. Feature flags can be used to disable problematic services and maintain system availability quickly.

• Development methodology. CI/CD pipelines are adapted to handle multiple deployment targets, implementing blue-green deployments for ML models. Version control includes both code and model artifacts, ensuring reproducible builds.

Compound AI system topologies and integration

Three primary architectural patterns are emerging in compound AI systems, each addressing different complexity requirements and communication needs.

1. Pipeline architectures

Linear pipeline topologies excel for sequential processing workflows. Components pass data through well-defined interfaces, enabling clear dependency management and straightforward debugging.

class ComponentInterface:
    def __init__(self, config: Dict):
        self.config = config

    async def process(self, input_data: Any) -> Any:
        # Component-specific logic
        return processed_data

    def health_check(self) -> bool:
        return True

# Pipeline orchestration
async def execute_pipeline(data, components):
    for component in components:
        data = await component.process(data)
    return data

Mesh architectures

For complex interdependencies, we implement mesh topologies where components communicate bidirectionally. This pattern supports dynamic routing and parallel processing but requires sophisticated orchestration.

API design patterns

We leverage multiple communication protocols optimized for different scenarios:

• RESTful APIs for stateless, cacheable interactions

• gRPC for high-performance, type-safe service communication

• GraphQL for flexible data fetching with precise field selection

# BentoML service example
@bentoml.service
class AIComponent:
    @bentoml.api
    def predict(self, input_data: np.ndarray) -> Dict:
        return {"prediction": self.model.predict(input_data)}

AI component coordination and communication

Effective component coordination requires careful attention to interface design and communication patterns.

API design strategy

We recommend implementing a contract-first approach using OpenAPI specifications. Define your service contracts before implementation:

# Example API contract
/agents/{id}/execute:
  post:
    requestBody:
      content:
        application/json:
          schema:
            type: object
            properties:
              task: { type: string }
              parameters: { type: object }

Data exchange format considerations

For JSON, use structured schemas with validation:
{
  "agent_id": "reasoning-001",
  "task_type": "analysis",
  "payload": { "data": "...", "context": "..." },
  "timestamp": "2025-07-11T10:30:00Z"
}
For Protocol Buffers, define message types for type safety:
message AgentRequest {
  string agent_id = 1;
  string task_type = 2;
  google.protobuf.Any payload = 3;
}

State management across components

Implement event sourcing for distributed state consistency. Maintain component state through:

1. Local state: Each service manages its immediate data

2. Shared state: Use Redis or etcd for coordination data

3. Event streams: Kafka for state change propagation

Function Calling and Parameter Negotiation

Here's our pattern for component interaction:

async def call_component(target_service, function_name, parameters):
    # Parameter validation and type checking
    validated_params = validate_schema(parameters, function_schema)

    # Async call with timeout and retry logic
    response = await http_client.post(
        f"{target_service}/execute/{function_name}",
        json=validated_params,
        timeout=30
    )

    return parse_response(response)

Compounding AI challenges: Vector embeddings and knowledge representation

Vector embeddings serve as the foundational layer for knowledge representation in compound AI systems, particularly when implementing RAG (Retrieval-Augmented Generation) architectures. In these systems, we map diverse knowledge sources into consistent high-dimensional vector spaces, enabling semantic understanding across multiple AI components.

When building compound systems, similarity search algorithms like FAISS or Annoy can be implemented to facilitate component interaction. For instance, a document retriever uses cosine similarity to identify relevant passages: similarity = dot(query_embedding, doc_embedding) / (||query|| * ||doc||). This enables real-time knowledge retrieval with sub-100ms latency.

A critical challenge we face is embedding space alignment between different models. When integrating a BERT-based retriever with a GPT-based generator, Linear transformations or dedicated alignment models can ensure semantic consistency across the pipeline.

Technical considerations for efficient vector operations include quantization techniques (reducing float32 to int8), hierarchical navigable small world graphs for fast retrieval, and batched operations to leverage GPU parallelization. We implement dynamic embedding caching with Redis to avoid recomputing vectors for frequently accessed documents.

In compound architectures, vector embeddings enable seamless information flow between specialized components—from knowledge extraction and storage to contextual retrieval and response generation. This creates robust AI systems that combine multiple models' strengths while maintaining semantic coherence across the entire pipeline.

Compounding AI reliability and scalability considerations

When deploying AI systems at enterprise scale, we must architect for both fault tolerance and dynamic scaling. Implementing circuit breaker patterns is crucial for preventing cascading failures across distributed AI workloads. Tools like Netflix's Hystrix or resilience4j provide production-ready implementations that automatically isolate failing services.

For horizontal scaling, leverage Kubernetes' Horizontal Pod Autoscaler (HPA) combined with custom metrics from AI inference loads. Vertical scaling becomes critical for GPU-intensive workloads—I recommend implementing resource quotas and limits using Kubernetes ResourceQuotas alongside NVIDIA's Multi-Instance GPU (MIG) for optimal GPU utilization.

Load balancing requires specialized approaches for AI workloads. We implement session-affinity load balancers for stateful models while using round-robin or least-connections algorithms for stateless inference services. NGINX or Envoy proxy works well for this purpose, especially when integrated with service mesh solutions like Istio.

Dynamic service discovery through tools like Consul or Kubernetes' native service discovery ensures our AI microservices can locate dependencies automatically. We implement health checks that monitor both system metrics and model accuracy thresholds.

Resource allocation optimization involves setting appropriate CPU/memory ratios for different AI workload types. It is recommended to allocate 4:1 CPU-to-memory ratios for inference services and 1:8 ratios for training workloads. Implementing pod priority classes ensures critical inference services get resource precedence over batch training jobs.

Quality of Service balancing requires implementing multiple deployment environments with canary releases, allowing for reliability while continuously improving model performance.

Building the future of AI with Snyk

Compound AI systems represent a paradigm shift in architecture, using multiple agents and tools to execute complex tasks. However, this modularity significantly expands your attack surface, especially since nearly half of AI-generated code can contain vulnerabilities.

You need automated guardrails to ensure that security is embedded at the moment of code generation, not bolted on afterward.

Want to learn how to secure your compound AI systems by getting the essentials for embedding security directly into your AI workflows? Download AI Code Guardrails: Best Practices.