Category: Agentic AI
Articles tagged Agentic AI
Elastic-caveman: Cutting AI response tokens by 64% without losing the best of Elastic
Learn how to use elastic-caveman to cut AI response tokens while keeping the Elastic agentic brilliance.
How to build agentic AI applications with Mastra and Elasticsearch
Learn how to build agentic AI applications using Mastra and Elasticsearch through a practical example.
Creating an Elasticsearch MCP server with TypeScript
Learn how to create an Elasticsearch MCP server with TypeScript and Claude Desktop.
The shell tool is not a silver bullet for context engineering
Learn what context-retrieval tools exist for context engineering, how they work, and their trade-offs.
Using Elasticsearch Inference API along with Hugging Face models
Learn how to connect Elasticsearch to Hugging Face models using inference endpoints, and build a multilingual blog recommendation system with semantic search and chat completions.
AI agent memory: Creating smart agents with Elasticsearch managed memory
Learn how to create smarter and more efficient AI agents by managing memory using Elasticsearch.
The Gemini CLI extension for Elasticsearch with tools and skills
Introducing Elastic’s extension for Google's Gemini CLI to search, retrieve, and analyze Elasticsearch data in developer and agentic workflows.
Agent Skills for Elastic: Turn your AI agent into an Elastic expert
Give your AI coding agent the knowledge to query, visualize, secure, and automate with Elastic Agent Skills.
SearchClaw: Bring Elasticsearch to OpenClaw with composable skills
Give your local AI agent access to Elasticsearch data using OpenClaw, composable skills, and agents, no custom code required.
Building effective database retrieval tools for context engineering
Best practices for writing database retrieval tools for context engineering. Learn how to design and evaluate agent tools for interacting with Elasticsearch data.
Build task-aware agents with an expanded model catalog on Elastic Inference Service (EIS)
Elastic Inference Service (EIS) expands its managed model catalog, enabling teams to build production-ready agents with flexible model choice across retrieval, generation, and reasoning, without managing GPUs or infrastructure.
