Elasticsearch lifecycle control plane at firehose scale with AI-powered SRE

Find out how Sprinklr operates Elasticsearch at firehose scale with AI-powered SRE workflows using MCP tools. A single enterprise tenant can hold massive social listening volume across a cluster, making cluster health, shard allocation, node performance, and data lifecycle challenging to investigate manually. We'll explore how agentic AI skills use MCP to investigate cluster health, nodes, shards, allocation, and production issues, turning operational data into actionable SRE workflows. We'll also cover the Elasticsearch architecture underneath it, including time-sharded partner-scoped indices, hot/cold allocation, S3 searchable snapshots, and the mechanisms that keep large-scale data searchable. See real-world agent investigations, production lessons, and a practitioner's take on building AI-assisted SRE workflows with Elasticsearch in 9.x.