CAG Agents

Cache-Augmented Generation (CAG) Agents are designed to optimize the speed and efficiency of AI-driven information retrieval systems. This service enhances standard RAG or other generative AI models by intelligently caching frequently accessed information and previously generated responses. This significantly reduces latency for common queries and lowers computational costs.

The technical approach involves implementing a smart caching layer that sits between the user query and the generative AI model. When a query is received, the CAG Agent first checks the cache for a relevant, up-to-date answer. If a valid cached response exists, it's served immediately. If not, the query proceeds to the full generation pipeline (e.g., RAG), and the new response is then considered for caching based on configurable policies like frequency, recency, and relevance.

CAG Agents are particularly beneficial for applications with high query volumes and a significant number of repetitive questions. Common use cases include customer service chatbots handling FAQs, internal helpdesks providing quick answers to common IT or HR issues, and public-facing information portals where response time is critical for user satisfaction. They can also reduce API call costs to underlying LLMs.

By integrating CAG Agents, businesses experience faster response times for their AI applications, leading to improved user experience and satisfaction. This approach also offers considerable cost savings by minimizing redundant computations and API calls to expensive generative models. The intelligent cache management ensures that users still receive accurate and relevant information while benefiting from enhanced performance.