Uploaded April 2026 | Updated September 2026, 3 weeks ago
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AI agents don’t have to be black boxes—or written in Python. In this session, we’ll build one from scratch in Kotlin using Koog, a lightweight framework for constructing tool-using LLM agents.
We’ll start by breaking down the fundamentals:
• How an agent communicates with a language model
• How tools are registered and invoked
• How to build robust interaction loops for both local LLMs and remote APIs like OpenAI or Google
From there, we’ll layer in Model Control Protocol (MCP) integrations and show how Koog’s DSL lets us define agent workflows as readable, reproducible graphs.
The session concludes with a live implementation of a coding agent—capable of generating and testing small programs—by wiring together tools such as test runners, linters, and file systems.
Please subscribe to our YouTube channel @ youtube.com/@DevoxxForever
Subscribe to LinkedIn @ linkedin.com/company/voxxed-days-amsterdam
Follow us on Twitter @ twitter.com/voxxedamsterdam
AI agents don’t have to be black boxes—or written in Python. In this session, we’ll build one from scratch in Kotlin using Koog, a lightweight framework for constructing tool-using LLM agents.
We’ll start by breaking down the fundamentals:
• How an agent communicates with a language model
• How tools are registered and invoked
• How to build robust interaction loops for both local LLMs and remote APIs like OpenAI or Google
From there, we’ll layer in Model Control Protocol (MCP) integrations and show how Koog’s DSL lets us define agent workflows as readable, reproducible graphs.
The session concludes with a live implementation of a coding agent—capable of generating and testing small programs—by wiring together tools such as test runners, linters, and file systems.
![[VDBUH2026] Brian Vermeer - Breaching LLM-Powered Apps: Overcoming Security and Privacy Challenges](https://i.ytimg.com/vi/iU0ytnOo6Qw/mqdefault.jpg "[VDBUH2026] Brian Vermeer - Breaching LLM-Powered Apps: Overcoming Security and Privacy Challenges
LLMs accessing the database and intelligent agents that perform online purchases? The possibilities for AI in applications seem endless but so are their security and data privacy risks. In this session, we’ll address common issues such as prompt injection, key leakage, abuse of private customer data for model training, legal restrictions, and more. In addition, we will show that general security issues in your systems can also influence the behavior and outcome of LLMs.
During this session, you’ll get a solid overview of the vulnerabilities to avoid, strategies to ensure data privacy compliance and best practices for building secure LLM-powered applications.")
