EventIn person

Aug: Agentic on K8s + swag

AUG4
18:00
  • This event focuses on Kubernetes and related technologies with a social element including food and swag sponsored by Komodor.
  • It is for developers, operators, and tech enthusiasts interested in Kubernetes and cloud-native infrastructure.
  • Attendees can network and share knowledge about container orchestration and deployment in person in Cambridge, MA.

About this event

Komodor is sponsoring food & bev this time - thank you Komodor!!

As usual

  1. 1.RSVP's close 48 hrs before the event. Please make it easy on us by RSVP'ing only if you intend to show up.
  2. 2.Don't be late! You'll get locked out, and that's no fun!
  3. 3.===

Agenda

6:00 pm: food / drinks / networking
6:20 pm: Talk #1: Building Agentic AI with Mickael Alliel
7:00 pm: Who is hiring? Who is looking?
7:10 pm: Talk #2: TBD
7:50 pm: Talk #3: Build an AI agent, win a prize with Harout Parseghian
8:30 pm: Finish

Speakers & topics

1\. How to Build Quality\-Driven Agentic AI in Noisy Big Data Environments, Mickael Alliel, Komodor

Building reliable agentic AI systems in production environments presents unique challenges when dealing with massive, noisy datasets. This talk shares hard-won lessons from developing Klaudia, Komodor's AI agent that processes millions of Kubernetes events daily to deliver autonomous troubleshooting with 95%+ accuracy.

The fundamental challenge isn't LLM capability—it's building systems that maintain reliability when 90% of your data is noise. We'll explore why most agentic AI fails in production: hallucinations masquerading as insights, inability to validate reasoning chains, and the brittle nature of RAG systems when dealing with complex, interconnected failure modes.

This session covers practical know-how learned through painful production iterations: how to build validation frameworks that catch LLM errors before they reach users, architectural patterns for constraining problem spaces without losing effectiveness, and techniques for creating evidence-based reasoning that can be audited and improved systematically.

You'll learn specific strategies for LLM validation in high-stakes environments, including confidence scoring systems, multi-agent verification patterns, and iterative investigation loops that prevent runaway reasoning. We'll cover the hard-earned lessons about what works and what spectacularly fails when building trustworthy AI agents

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