Reliable, trustworthy AI starts with context engineering, the practice of building systems that give AI agents the precise, grounded information and tools they need to complete complex tasks accurately. Elasticsearch connects data, retrieval, tools, and memory — the essential components that enable models to learn, reason, and act from the right information.

But context engineering doesn't end at the data layer. Inside Cursor, it shows up as a continuous loop: An agent reads code, runs tests, observes logs, surfaces alerts, and proposes fixes all without leaving the editor. Rather than a single retrieval augmented generation (RAG) query that returns a static answer, Cursor's harness efficiently orchestrates which tools agents invoke and when, including Elastic skills, linters, test runners, repo search, based on what the task actually demands.

Elastic's new plugin on the Cursor Marketplace brings these two halves together. It packages Elastic's platform expertise as first-class Agent Skills and a built-in Elastic Docs MCP server, removing the architectural friction of separate, disconnected platforms. Cursor can now discover and invoke Elastic's data-aware tools on demand:

Search: Semantic hybrid search over private knowledge bases directly from the editor.
Query: Execute ES|QL queries against live production data mid-workflow.
Observe: Manage and surface Kibana dashboards when a build fails or a test breaks.
Secure: Triage security alerts and propose fixes grounded in the alert data that triggered them.

The result: Engineering teams get an agent that sees code and production reality in a single workspace, accelerating product innovation, reducing operational toil, and enabling more robust AI solutions at scale.

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Articles by Laurent Saint-Felix

Principal Software Engineer

Elastic and Cursor partner to accelerate context engineering with coding agents

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