At the time of the conversation, Philipp Krenn was Director of Developer Relations at Elastic. On Chain of Thought he dug into the trust and reliability gap with AI agents and what it takes to build the testing, feedback, and guardrails that production agents need.
Episodes
What the conversations cover
These notes summarize the episodes featuring Philipp Krenn, including other speakers in panel conversations. Follow a source link to read the discussion in context.
EP 32 The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn
- Elasticsearch quietly powers search across much of the internet. Philipp Krenn notes that Wikipedia and Stack Overflow run Elasticsearch behind their search boxes, and that almost everything on GitHub is cached or powered by Elasticsearch in the background — usage most people never see. Episode takeaways
- MCP is changing how systems are accessed, not just what data they hold. Krenn frames it as a shift in interaction mode: rather than writing against one specific REST API, you let the LLM figure out the MCP connection, fetch the right data, and see what actions it can run — including descriptive asks like “build me a Kibana dashboard.” Episode takeaways
- Elastic does not build large language models. Krenn says the models Elastic builds are for inference and re-ranking, and it relies on partners to supply the LLM that generates the answer — or, in Galileo’s case, the Luna models on the evaluation side. He describes the stack as an “AI lasagna” of layers where each player partners with the others. Episode takeaways
In their words
Maybe AI even puts you in a better position there because like with the hallucinations and giving wrong answers, like you have an even stronger business impact. Yeah. Or like business drive to give the right answers.
Philipp Krenn The AI Agent Trust Gap: Bridging Risk to Reliability | Elastic’s Philipp Krenn · 34:49 Transcript excerpts; ellipses mark omitted words. Some automatically selected quotes have filler words removed.
Explained on AI, decoded
- Should you use MCP or build a custom integration to connect AI to your tools?
- What's the difference between AI observability, evaluation, and benchmarking?
- What are AI agent guardrails, and how do you set them?
- What is AI observability, and why do you need it in production?
- How do you govern AI agents in an enterprise?