The hidden reason your AI assistant feels so sluggish
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AI workloads are exposing fundamental mismatches in traditional database architectures. When AI agents query databases, they issue dozens of concurrent queries in rapid succession rather than single heavyweight queries, overwhelming systems built for batch reporting. The emerging solution is a Postgres + OLAP architecture (e.g., ClickHouse) that separates transactional and analytical workloads. Observability faces the same problem: the classic metrics/logs/traces model doesn't support the high-cardinality, long-retention data AI-driven SRE workflows need. The convergence of observability and data warehousing around columnar engines, open table formats like Iceberg, and native agent interfaces (MCP) is accelerating. Teams that delay migrating off legacy platforms risk being unable to handle agentic query volumes at scale.
Table of contents
Agents don’t query like humansPostgres + OLAP Is becoming the defaultObservability runs into the same problemTwo categories and one set of requirementsThe cost of waiting is going upSort: