Systems | Development | Analytics | API | Testing

Every engineering team is onboarding AI agents. Most are doing it without a governance model - static API keys, no audit trail, no way to revoke access if something goes wrong. Join us on April 15th as we go live on the topic everyone is talking about but few are solving: how to govern AI agent access to streaming data.

AI agents need access to your systems. But are you sure they're accessing them securely? In this video, Tun @DataSurfer breaks down the way most teams give AI agents access today: static API keys, shared credentials, no audit trail. It's a disaster waiting to happen, but what exactly can teams do about it?

Agents need real access to do real work - but when MCP connects your AI to production systems like Kafka, who controls what it can touch? OAuth 2.1 is emerging as the answer.

2025 was vibe coding. 2026 is Agentic Engineering - and the security rules haven't caught up. AI agents now have direct access to your databases, your APIs, your Kafka clusters. The protocol giving them that access is MCP. And most teams have no idea how exposed they are. We are fixing this problem with OAuth 2.1.

Migrating Kafka clusters doesn't have to be a complex or high-risk operation. In this technical walkthrough, we demonstrate how Lenses K2K managed through Lenses 6 simplifies the migration of mission-critical banking applications from Strimzi to AWS Express Brokers with minimal downtime and zero data loss.

In this episode, Drew Oetzel sits down with Ryan Anguiano, Staff Architect at @WhatIfMediaGroup to discuss their massive migration of from legacy EC2 instances to using the @Strimzi operator. Ryan shares deep technical insights into how they optimized their data streaming architecture, including their use of EKS, EBS storage striping, and why the 12-Factor App methodology was the key to migrating over 100 services in just a few months.

AI agents are only as good as the data they can access. In this video, we explore the Model Context Protocol (MCP) and how it creates a bridge between AI models and Apache Kafka. Learn how MCP allows AI agents to securely produce, consume, and manage Kafka topics in real-time—transforming your event streams into actionable context for LLMs.