Modern data platforms face challenges with query performance tuning and system price-performance, especially with increasing agentic workloads. Manual query optimization is becoming impractical as data volume, variety, and velocity grow, and queries are generated automatically by agents. BigQuery has evolved into a primary engine for the Agentic AI era, focusing on autonomous query processing. Its disaggregated storage and compute architecture, serverless processing, and fine-grained compute management contribute to optimal price-performance. Key innovations include a Self-Learning Engine with History-Based Optimizations (HBO) that learns from past query executions to apply beneficial optimizations automatically without user intervention. This system includes safety guardrails to prevent regressions, rejecting optimizations that don't improve performance. The advanced runtime enhances vectorization and implements short query optimizations, accelerating eligible queries and reducing slot usage. These improvements apply consistently across different data formats, including open lakehouse architectures. Fluid scaling enhances autoscaling, enabling per-second billing for compute resources and reducing costs. These autonomous capabilities are crucial for the future of data platforms as AI agents require significantly higher query latency and concurrency than human users. BigQuery's goal is to automatically and safely optimize queries, allowing users to focus on delivering better experiences rather than manual infrastructure management and query optimization.