I built the same AI agent twice.
Once with just an LLM. Once powered by @Elastic .
Same question: "Which clients have the most risk exposure from negative news?"
The basic agent gave me a textbook answer. Generic. No real data. The kind of response that sounds smart but helps nobody.
The Elastic agent pulled live portfolio data, cross-referenced news sentiment across four indices using LOOKUP JOINs, calculated actual dollar exposure, and ranked clients by risk - in seconds.
Same model. Same prompt. Completely different result.
The difference? Context.
Here's what makes Agent Builder different from every other agent framework I've tried:
→ You write real business logic in ES|QL - including multi-index joins that would normally require a data warehouse
→ Parameterized queries act as guardrails so the LLM can't go off-script with your data
→ You register queries as Tools with natural language descriptions - the agent reads the description and decides when to call it
→ Custom Agents get a full system prompt (persona, reasoning framework, output rules) so they behave like a specialist, not a generic chatbot
→ Built-in hybrid search combines vector, text, and structured search for higher relevance out of the box
→ Everything you build is instantly available over MCP, A2A, and REST API - plug your agent into Claude Desktop, Cursor, LangChain, or your own app with zero extra work
Elastic has 15+ years of search relevance, 100M+ weekly downloads, and they've been powering RAG and vector search longer than most companies have been talking about it. Agent Builder is them packaging all of that into something you can actually build agents on top of.
The model isn't the edge. The context infrastructure is.
Try Elastic Agent Builder free → https://cloud.elastic.co/registration?utm_source=linkedin&utm_medium=influencer_marketing&utm_campaign=tech-with-tim-agent-builder-gc&utm_content=context-video
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