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Once your LLM agent starts acting on its own — calling tools, writing files, hitting APIs — "where do I run this thing?" becomes a security question, not a deployment footnote.

This free, 2-hour hands-on workshop tackles that exact problem with Docker's Dan Ndombe, using Docker Sandboxes (SBX) to give agents an isolated runtime with clear boundaries on what they can touch.

What You'll Learn
🔹 How to spin up a sandboxed agent runtime using Docker Sandboxes
🔹 How to assign an agent a real task and watch it execute inside the sandbox
🔹 How to tighten policies and permissions around agent behavior
🔹 A reusable containment pattern for your own agent prototypes

Why This Matters
An agent with unrestricted system access is an agent with an unrestricted attack surface. A single bad tool call or prompt injection can turn into a real incident. Instead of trusting an agent by default, you contain it by design — and that's exactly what this session walks you through, live and code-along, not slide-by-slide.

Who Should Come?
Software engineers building agents, DevOps/platform engineers, security-minded developers, and technical leads scaling agentic AI. No prior Docker experience required.

Bring your questions — we'll leave time for live Q&A.

Related topics

Machine Learning
Natural Language Processing
Business Analytics
Data Mining
Data Science

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