Small language models (SLMs) combined with retrieval-augmented generation (RAG) offer enterprises a cost-effective alternative to large language models for production AI systems. This modular, agent-based architecture provides predictable infrastructure costs, lower latency, and better auditability by decomposing AI
Table of contents
Why This Matters to ArchitectsUnderstanding SLM and RAGModular Agentic ArchitectureCommunication and InteroperabilityGovernance and Structured AutonomyDeployment Patterns and ScalabilityObservability and Operational ExcellenceCase Study: Compliance Monitoring at ScaleLessons Learned and Trade-OffsFuture DirectionsConclusionSort: