Uploaded July 2026 | Updated September 2026, 2 weeks ago
Docker Sandboxes exist because of a simple problem: the moment your LLM stops just answering questions and starts acting — calling tools, writing files, hitting APIs, making decisions on its own — you need somewhere safe to let it do that. An agent with unrestricted access to your systems is an agent with an unrestricted attack surface, and that’s exactly the gap this workshop closes.
This free, hands-on session is Part 2 of the Docker webinar series. Over two hours, Docker’s Dan Ndombe will walk you through running LLM agents safely using Docker Sandboxes (SBX) — a practical, code-along workshop rather than a slide deck. No prior Docker experience is required.
Once you understand how large language models work, the next logical question is where you run them. When an agent can execute code, browse the web, or touch your file system, “where” is no longer a deployment detail — it’s a security decision.
They give agents an isolated runtime with clearly defined boundaries: what they can access, what they can execute, and what they absolutely cannot touch. Instead of trusting an agent by default, you contain it by design. A single bad tool call, a malicious prompt injection, or an overly broad permission set stays boxed in, instead of turning into an incident.
This session is deliberately hands-on. By the end, you’ll have a repeatable pattern you can take straight back to your own projects. Specifically, you’ll:
- Spin up a sandboxed agent runtime using Docker Sandboxes (SBX)
- Assign a real agent a small, practical task and watch it execute inside the sandbox
- Explore how to tighten policies and permissions around agent behavior
- Walk away with a reusable pattern for deploying this approach in your own AI and agent prototypes
- This isn’t abstract theory — it’s a live, practical demonstration of containment and control that you can replicate immediately on your own machine.
About the speaker: docker.com/contributors/dan-ndombe
#docker #dockertutorials
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👉 Learn more about Data Science Dojo here:
datasciencedojo.com
👉 Watch the latest video tutorials here:
datasciencedojo.com/tutorials
👉 See what our past attendees are saying here:
https://datasciencedojo.com/data-scie...
At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 8000+ employees from over 2000+ companies globally, including many leaders in tech like Microsoft, Apple, and Facebook.
🔗 Subscribe to our newsletter for data science content & infographics: datasciencedojo.com/newsletter
Docker Sandboxes exist because of a simple problem: the moment your LLM stops just answering questions and starts acting — calling tools, writing files, hitting APIs, making decisions on its own — you need somewhere safe to let it do that. An agent with unrestricted access to your systems is an agent with an unrestricted attack surface, and that’s exactly the gap this workshop closes.
This free, hands-on session is Part 2 of the Docker webinar series. Over two hours, Docker’s Dan Ndombe will walk you through running LLM agents safely using Docker Sandboxes (SBX) — a practical, code-along workshop rather than a slide deck. No prior Docker experience is required.
Once you understand how large language models work, the next logical question is where you run them. When an agent can execute code, browse the web, or touch your file system, “where” is no longer a deployment detail — it’s a security decision.
They give agents an isolated runtime with clearly defined boundaries: what they can access, what they can execute, and what they absolutely cannot touch. Instead of trusting an agent by default, you contain it by design. A single bad tool call, a malicious prompt injection, or an overly broad permission set stays boxed in, instead of turning into an incident.
This session is deliberately hands-on. By the end, you’ll have a repeatable pattern you can take straight back to your own projects. Specifically, you’ll:
- Spin up a sandboxed agent runtime using Docker Sandboxes (SBX)
- Assign a real agent a small, practical task and watch it execute inside the sandbox
- Explore how to tighten policies and permissions around agent behavior
- Walk away with a reusable pattern for deploying this approach in your own AI and agent prototypes
- This isn’t abstract theory — it’s a live, practical demonstration of containment and control that you can replicate immediately on your own machine.
About the speaker: docker.com/contributors/dan-ndombe
#docker #dockertutorials
-----------
👉 Learn more about Data Science Dojo here:
datasciencedojo.com
👉 Watch the latest video tutorials here:
datasciencedojo.com/tutorials
👉 See what our past attendees are saying here:
https://datasciencedojo.com/data-scie...
At Data Science Dojo, we believe data science is for everyone. Our in-person data science training has been attended by more than 8000+ employees from over 2000+ companies globally, including many leaders in tech like Microsoft, Apple, and Facebook.
🔗 Subscribe to our newsletter for data science content & infographics: datasciencedojo.com/newsletter
