Written by Alex Merced Developer from devNursery.com and alexmercedcoder.dev You should follow him on Twitter and checkout his articles on LogRocket.
The real bottleneck with enterprise AI isn't retrieval augmentation or model accuracy : it's governance. Policy-as-code (OPA, ABAC, Rego) lets you enforce access rules, column masks, and row filters on any data source at query time.
A deep dive into the four categories of agentic coding tools dominating 2026: CLI agents, desktop IDEs, 24/7 autonomous agents, and model routers. 40+ tools compared.
The four-layer architecture of the agentic lakehouse: object storage, Apache Iceberg table format, Apache Polaris catalog, and the semantic/agent layer. How each layer provides guarantees for AI agent access.
How Iceberg optimistic concurrency control, partition-level isolation, and idempotency keys enable safe concurrent writes from multiple AI agents to the same lakehouse tables.
Lakehouse context layers bridge the gap between raw Iceberg tables and AI agents that need business meaning. Atlan and Snowflake Horizon each take different approaches to the same problem.
ClickHouse has become the leading real-time analytics engine for AI agent workloads, with event-loop architectures that let agents query and act at sub-second speeds.
