Category: Agentic AI
Articles tagged Agentic AI
Extract chart data standard OCR misses: Elastic Agent Builder and LlamaParse in one pipeline
Build an end-to-end pipeline that extracts structured data (including values from charts) out of complex PDFs and into Elasticsearch, ready for agent queries with ES|QL.
How we built a persistent agent memory layer on Elasticsearch with 0.89 recall and zero tenant leaks
Discover the architecture behind a persistent, multi-tenant agent memory layer on Elasticsearch: three indices, hybrid retrieval with RRF and a reranker, supersession, decay, and per-user DLS isolation. R@10 0.89 across 168 questions. Full open-source implementation included.
Systematic research with LangChain's Deep Agents framework and Elasticsearch
Building a systematic research pipeline using LangChain's Deep Agents framework and Elasticsearch:
Your AI agent reads the fine print: building a RAG pipeline over EU regulations with Elasticsearch and OGX
Learn how to configure Elasticsearch as an OGX vector store, ingest EU regulation PDFs and build a Python RAG agent that runs hybrid BM25 and vector search with source-level citations.
Best practices for building a modern app with vector search
Exploring six vector search tips for building modern AI search applications entirely on Elasticsearch, with an opinionated rationale at each architectural decision.
Your Elastic agent, Google's ADK, and zero custom APIs: building “Lucky Planet” over A2A
Elastic Agent Builder's native A2A endpoint lets Google's ADK orchestrate a remote agent, with no custom REST API. Watch it work in 'Lucky Planet,' a random-exoplanet game built end-to-end.
137,000 people, zero human decisions: agentic disaster response with Elasticsearch
Find out how a Kibana detection rule, a workflow and an AI agent automatically relocated 137,000 military personnel across seven installations when a hurricane hit, no dispatcher required.
Build a RAG agent with Elasticsearch and GitHub Copilot SDK
Wire Elasticsearch into the GitHub Copilot SDK as a RAG tool in five lines of C#, grounding your agent in your own logs, docs and data instead of model training data.
Cutting agent costs with pre-computed context
Pre-computing context as Knowledge Indicators reduces LLM agent token costs by up to 75% and improves answer accuracy from 60% to 92%. This post covers the extraction, retrieval and feedback loop that make it work, tested against the BrowseComp-Plus benchmark.
Elastic Agent Builder: How we taught AI agents to manage their own context
Agent Builder in Elasticsearch 9.4 ships dynamically loaded skills, a conversation context store, selective compaction, and external connectors to cut token costs by 40% and let agents handle their own context management.
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.
