Part 10/14:
In this context, the core pillars—data freshness, query latency, and concurrency—remain central. As billions of agents or users query data simultaneously, systems must respond swiftly to maintain conversational fluidity.
AI and Real-Time Analytics: Building Smarter Agents
The integration of AI, especially LLMs, introduces new capabilities such as:
Retrieval-Augmented Generation (RAG): Combining AI responses with up-to-date data retrieved in real time.
Vector Embeddings: Enhanced search capabilities within voluminous data, enabling similarity searches and contextual understanding.
Automated Data Interaction: AI-driven agents can execute queries, mutate data, and generate insights autonomously by leveraging systems like Pino.