Building Trust in the Machine: A Guide to Architecting Agentic AI for SRE

Architecting agentic AI for Site Reliability Engineering (SRE) presents both opportunities and challenges, particularly in complex, cloud-native environments like Kubernetes. While the promise of AI in SRE is attractive, naive implementations of Large Language Models (LLMs) can lead to issues such as hallucinations, context window saturation, and unreliable outputs without rigorous data engineering. The development of Klaudia, an agentic AI by Komodor, aims to address these challenges by structuring AI as a family of specialized agents, each with domain-specific expertise, coordinated by an orchestrator agent. This multi-agent architecture, alongside a stringent "Swiss Cheese" validation model involving multiple layers such as local development, golden standards, shadow agents, and LLM evaluations, ensures reliability and precision. A hybrid approach combining traditional machine learning with LLMs enhances Klaudia's ability to filter and analyze vast datasets, achieving precision akin to traditional Root Cause Analysis tools. The focus on trust and safety over breadth is emphasized, with the AI designed to provide transparent and evidence-backed recommendations, ultimately prioritizing a "do no harm" philosophy.