AI connectors in Kibana: 90+ data sources your agent can call
Your index has the alert, but the Slack thread, GitHub PR, Confluence doc and PagerDuty timeline that explain it sit elsewhere. AI connectors are how your agent reads all four while it reasons.
From a checkout alert to the failing span: Investigating with the Elastic AI Agent and Elastic APM flyouts
We didn’t take the agent’s root cause on trust. The service map and the trace backed it up, and one ES|QL query put a number on it: 80 of 138 gold-tier charges failed, and no other tier did.
How our agent skills research, build, and test a complete Elastic integration
We pointed our agent skills at the NIST NVD API and got two data streams and a validated CEL program, with pipeline and system tests passing. The research showed 78% of a day’s CVE records are republications of older ones, which changed the design.
We cut 40% of our MCP tools to get into Claude’s connector directory
Anthropic’s reviewers read every tool in our MCP connector and sent back fixes, wording changes and a case for publishing fewer of them, which is part of why mcp.elastic.co is now on the roadmap.
Bringing Elastic into Claude: The ‘why,’ the ‘how,’ and the ‘gotchas’
Point your Claude plugin at main and every merge ships as a release, and that’s the least surprising thing that happened between submitting and going live.
Your AI chat is not a screenshot: Live shared conversations in Elastic Agent Builder
Whoever picks up the problem next reads the whole thread and carries on with the same agent, with no access to anything beyond that one conversation.
How we’re building a context engine to cut agent token use by 71%
With a context engine, the same agent and model answered in 64 seconds using 9 tool calls. The baseline took 213 seconds, 33 calls, and missed a key relationship entirely.
Ask Elastic Agent Builder why it's slow: Natural-language trace analysis
Four agent performance questions your Agent Builder traces can answer, covering token spend by model, tool error rates, slow conversation turns, and recent prompts. The ES|QL for each is here, including the type cast SUM() needs.
Agentic workflows in Elasticsearch: pause an AI agent for human approval, resume 72 hours later
Build AI agent orchestration where the workflow waits for a human approval and then executes the fix on its own, with nothing extra to provision and the whole decision trail queryable in Elasticsearch.
You and your AI agent shouldn't be using curl: Introducing the Elastic CLI and Agent Skills
Elastic CLI reaches every Elasticsearch, Kibana and Cloud API from one command, and it's what Elastic Agent Skills run on. Input is validated against a JSON Schema before anything leaves your machine, and API keys stay in your OS keychain.
Trust, but benchmark: How we let an AI agent optimize Elasticsearch
We share how we built a harness that automatically identifies and implements optimizations in the Elasticsearch codebase.
AI root cause analysis in Elastic Agent Builder that cites its evidence
The new release failed at 27.2%, the old one at 28.2%, so the deploy was never the cause; the agent worked that out in 72 seconds and handed back a trace ID for the failure that was.