{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Programming Tech Brief By HackerNoon","title":"Runbooks + RAG: How I Gave My AI SRE Agent the Context It Was Missing","html":"","width":"100%","height":180,"duration":483,"description":"\n This story was originally published on HackerNoon at: https://hackernoon.com/runbooks-rag-how-i-gave-my-ai-sre-agent-the-context-it-was-missing.\nLearn how retrieval-augmented generation (RAG) helps AI SRE agents use runbooks, postmortems, and documentation to investigate real production incidents.\nCheck more stories related to programming at: https://hackernoon.com/c/programming.\n You can also check exclusive content about #site-reliability-engineering, #devops-knowledge-retrieval, #ai-postmortem-retrieval, #vector-database-runbooks, #sre-agent-architecture, #ai-runbook-retrieval, #ai-sre-agent, #hackernoon-top-story, and more.\nThis story was written by: @armeesala. Learn more about this writer by checking @armeesala's about page,\n and for more stories, please visit hackernoon.com.\nLarge language models understand infrastructure, but they don't understand your infrastructure. This article explains how a retrieval-augmented generation (RAG) layer gives AI SRE agents access to runbooks, postmortems, architecture docs, and service ownership information at investigation time. It covers the two-plane knowledge model, vector retrieval, real-world failure modes, and practical lessons for keeping an AI knowledge base accurate, current, and trustworthy.\n \n ","thumbnail_url":"https://img.transistorcdn.com/KhCapPSRkLGL2Xw8888yuChkNRWthaKapLYTvNdu4W4/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxMTY2LzE2ODM1/ODIzMzAtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}