Most AI SRE tools sound smart but fall apart in real incidents. Manveer Chawla explains why the problem isn’t the model, it’s the observability foundation, and how fixing that changes everything.

ClickHouse is a leading platform for open-source columnar database technology and real-time analytics. With ClickHouse, developers can delve into documentation, case studies, and performance benchmarks to learn how to build highly scalable and performant analytical systems. It provides tips about ClickHouse's storage engine, query execution, and distributed architecture. Developers can gain insights into their data in real-time, enabling them to make informed decisions and extract  business insights.

ClickHouse

AI SRE tools fail not because of weak models, but because they lack proper observability foundations. Legacy systems with short retention windows, dropped high-cardinality data, and slow queries prevent AI from performing effective root cause analysis. ClickHouse's columnar architecture enables long-retention, high-cardinality observability at scale with sub-second query speeds, making it ideal for AI SRE copilots. The article presents a reference architecture combining ClickHouse with context layers (deployments, topology, incident history) and LLMs via SQL to create an investigative copilot that correlates data and surfaces insights while keeping humans in control of remediation decisions.

Your AI SRE needs better observability, not bigger models.

What causes AI SRE tools to fail in production #

Why ClickHouse is the right database for building an AI SRE Copilot #

The reference architecture: AI copilot for SRE #