Enrich Data with Inferred Context for Accurate Agent Reasoning

Traditional data strategies focused on clean, structured data with lineage and quality, achieving only 50% agent accuracy; the other 50% requires business context like hidden PDF codes or supply chain meanings previously siloed in human minds. Google's Knowledge Catalog uses GenAI to infer schemas, descriptions, and relationships across thousands of structured/unstructured files (e.g., PDFs), avoiding context window limits and high costs of feeding raw docs to models. This enables agents to access precise, low-cost context, boosting trust and efficiency over human-coded glossaries. Combined with semantic hybrid search from Google Search (including reranking), it serves optimal real-time context, preventing agents from getting lost in excess data.

Intent-Driven Agent Swarms via Tools and Integrations

Move beyond persona-based agents to swarms handling end-to-end tasks (data wrangling, modeling, visualization, deployment) with intent-driven engineering, freeing practitioners for outcomes over tasks. Data Agent Kit provides pre-built plugins/skills for natively orchestrating BigQuery, Spark, AlloyDB pipelines, and multi-tool actions into ledgers/marketing systems. Gemini Enterprise acts as the front door: business users chat with conversational agents backed by BigQuery/Looker assets or Deep Research agents blending enterprise data (via Knowledge Catalog) with web/docs for holistic insights (e.g., shipping optimization from weather/traffic + internal data) in seconds vs. weeks. This grounds agents in machine-readable, real-time data for true ROI.

Multi-Cloud Scale with Optimized Economics

Apache Iceberg open standard and Cross-Cloud Lakehouse unify data across AWS S3, Azure, Databricks Unity Catalog, Snowflake without migration, using Cross-Cloud Interconnect for subsecond latencies and petabyte-scale transfers, solving proprietary format silos. For agent-scale (10-20 API calls per human click, swarms spiking traffic), Google optimizes the full stack: BigQuery 35% faster queries/40% cost drop, Spark Lightning Engine 5x faster/2x price-performance vs. alternatives, 230x token reduction for AI inferencing. TPU v8 separates training/inference to avoid silicon bottlenecks. Vertical integration ensures seamless, cost-effective low-latency swarms, turning 45-minute human processes into 1-minute actions.