Build Cheaper, Safer, Auditable AI with SLMs and RAG

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 responsibilities across specialized components. Each agent operates with its own retrieval pipeline and governance controls, communicating through secure protocols like Agent2Agent and Agent Name Service. The approach enables graduated autonomy levels, horizontal scaling, and integration with existing enterprise observability and compliance frameworks, making it particularly suitable for regulated industries requiring verifiable, traceable AI outputs.