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Architecture Overview

Naas implements a sophisticated multi-agent architecture that enables complex AI workflows combining reasoning, tool usage, and collaborative problem-solving. The platform is built on proven open-source foundations while providing enterprise-grade scalability and security.

Platform Architecture​

The Naas platform consists of three interconnected layers that work together to provide comprehensive AI capabilities:

The three-layer architecture enables:

  • Separation of Concerns: Each layer has distinct responsibilities while maintaining clean interfaces
  • Scalability: Layers can be scaled independently based on demand
  • Flexibility: Components can be swapped or extended without affecting other layers
  • Security: Security controls can be applied at appropriate architectural boundaries

Core Components​

Multi-Agent System​

The heart of Naas's AI capabilities, built on LangGraph for stateful agent interactions. Agents can collaborate, delegate tasks, and maintain context across complex workflows.

Ontology Engine​

Formal knowledge representation using W3C standards (RDF/OWL) that enables semantic reasoning and data integration. The ontology serves as the "unifying field" connecting data, models, and workflows.

Memory & Context Management​

Persistent conversation history and knowledge retention using vector databases and graph storage. Enables agents to maintain context across sessions and learn from interactions.

Tool Integration Framework​

Standardized patterns for connecting external systems, databases, APIs, and services. Tools are automatically exposed to agents through the LangGraph framework.

Deployment Models​

Local Development​

  • ABI CLI: Complete AI operating system running locally
  • Jupyter Integration: Native notebook environment for rapid prototyping
  • Offline Capable: Full functionality without cloud dependencies
  • Model Flexibility: Support for local, cloud, or hybrid model deployment

Cloud Infrastructure​

  • Kubernetes Native: Container orchestration for production scaling
  • Multi-Region: Global deployment with data residency controls
  • Auto-Scaling: Dynamic resource allocation based on workload
  • High Availability: Built-in redundancy and failover capabilities

Hybrid Architecture​

  • Local + Cloud: Combine local development with cloud infrastructure
  • Edge Computing: Deploy agents closer to data sources
  • Air-Gapped: Complete offline operation for maximum security
  • Federated: Multi-organization deployments with shared ontologies

Integration Patterns​

Data Integration​

The platform supports multiple data integration patterns to connect with existing enterprise systems:

Integration capabilities include:

  • Universal Connectors: Python-based drivers for any system with an API
  • Real-Time Processing: Streaming data integration and event processing
  • Batch Processing: Scheduled data synchronization and bulk operations
  • Semantic Mapping: Automatic ontology alignment and data harmonization

Security Architecture​

Defense in Depth​

Multiple layers of security controls protect data and operations:

  • Network Security: TLS encryption, VPN access, network segmentation
  • Authentication: Multi-factor auth, SSO, certificate-based access
  • Authorization: Role-based access control, principle of least privilege
  • Data Protection: Encryption at rest and in transit, key management
  • Audit & Compliance: Comprehensive logging, compliance reporting

Zero Trust Model​

The platform implements zero trust principles:

  • Verify Explicitly: Every request is authenticated and authorized
  • Least Privilege: Minimal access rights for users and services
  • Assume Breach: Continuous monitoring and incident response

Performance Characteristics​

Scalability Metrics​

  • Agent Concurrency: 1000+ concurrent agents per cluster
  • Request Throughput: 10,000+ requests per second at API layer
  • Data Processing: Petabyte-scale knowledge graph operations
  • Response Latency: Sub-second agent response times

Resource Optimization​

  • Elastic Scaling: Automatic scaling based on demand patterns
  • Resource Pooling: Shared compute resources across agents
  • Caching Strategy: Multi-level caching for frequently accessed data
  • Load Balancing: Intelligent request distribution across nodes

Technology Stack​

Core Technologies​

  • LangGraph: Agent orchestration and state management
  • LangChain: LLM integration and tool framework
  • RDFLib: Ontology processing and semantic reasoning
  • FastAPI: High-performance API framework
  • PostgreSQL: Transactional data storage
  • Redis: Caching and session management

AI/ML Stack​

  • Multiple LLM Support: OpenAI, Anthropic, Google, Meta, Mistral
  • Vector Databases: Pinecone, Weaviate, Chroma for embeddings
  • Graph Databases: Neo4j, Amazon Neptune for knowledge graphs
  • Model Serving: Ollama, vLLM for local model deployment

Infrastructure​

  • Kubernetes: Container orchestration and service mesh
  • Docker: Containerization and deployment packaging
  • Helm: Kubernetes application packaging and management
  • Prometheus/Grafana: Monitoring and observability stack

Next Steps​

Explore specific architectural components:

Each section provides detailed implementation guidance with code examples and best practices.