Ken Exner

"We're kind of known for relevance. We've always been in the business of relevance. Traditional search was always about getting you the right 10 links, but in the age of AI, it's about making sure that you have the one best answer or the one most accurate task that an agent is performing. And we do that through relevance."

"I think the most important thing is relevance. And this is where we kind of shine because being a search engine, the most important thing for a search engine is relevance. And if you're trying to build a generative AI application and you want to pass context to an LLM, you want to ground it on the most relevant content."

"I think 2026 will be the year of context engineering. I really, really mean that. This year was the year of agents. Everyone started moving towards agents. I think as people start building agents, they're going to realize how critical, how vital context engineering is."

"I think 2024 was the year of experimentation and getting something out. 2025 is going to see us move towards, moving across the enterprise and seeing more use cases for generative AI."

"I personally think it's the most important thing in building AI applications that work, that succeed, that do the right things. You have to have the right data. So I think you're going to hear this term context engineering a lot over the next year, because context engineering is what is vital to doing AI right."

"One of the things that we do is we help ground LLMs using Retrieval Augmented Generation on companies ' private data. So for a lot of companies that already use Elastic, this becomes a very simple thing for them. They already managed their data with Elastic, they already index their data. Now they can use that same data and that same Elastic search engine to ground their LLMs, start building generative AI applications."

"I think there are two things that really led the way to this agentic boom that we had this year. One was the development of MCP, which created a standard interface, standard protocol for how you could express functions as an API. The other part was LLMs got really good at reasoning. So the combination of really good reasoning, plus an open standard for tool definition led to this huge boom of agents."

"So we have been moving aggressively into generative AI in both observability and security and starting to automate some of the workflows in kind of magical ways that surprise-"