Running LLM Agents Safely: Hands-On with Docker Sandboxes

Why LLM Agent Security Matters

As AI agents become more capable, they are increasingly being trusted to take actions on behalf of users—calling tools, writing files, executing code, accessing APIs, interacting with databases, and performing multi-step workflows with minimal human intervention. While these capabilities unlock powerful new possibilities for automation and productivity, they also introduce significant security challenges. The moment an LLM agent can take actions autonomously, developers must carefully consider where and how those actions are executed, what permissions the agent should have, and how potential risks can be contained. As a result, LLM agent security has become a critical consideration for organizations deploying AI-powered systems.

As organizations move from simple chatbot applications toward fully agentic AI systems, LLM agent security is becoming one of the most important aspects of AI application development. An unrestricted agent that can execute code, modify files, or access external services may inadvertently create vulnerabilities, misuse resources, or interact with systems in unintended ways. Building effective safeguards around AI agents is therefore critical for both experimentation and production deployments.

LLM Agent Security with Docker Sandboxes

This hands-on webinar explores one of the most practical and widely applicable approaches to LLM agent security: running LLM agents inside isolated Docker Sandboxes. Rather than focusing solely on theory, the session will guide participants through building a secure runtime environment where agents can safely perform tasks while operating within clearly defined boundaries and permissions.

Understanding LLM Agent Security Risks

We’ll begin by examining the unique security risks associated with autonomous AI systems. Participants will learn how modern LLM agents interact with tools, why unrestricted execution can be dangerous, and what types of safeguards are commonly used to reduce risk. We’ll discuss practical concerns such as file system access, code execution, network permissions, resource usage, and interactions with third-party APIs and external services. Understanding these challenges is essential for anyone interested in implementing effective LLM agent security practices.

Docker Sandboxes as a Foundation for LLM Agent Security

From there, we’ll introduce Docker Sandboxes (SBX) as a lightweight and effective mechanism for isolating agent behavior and reducing potential risks in production AI applications. Participants will learn how containerization provides a controlled environment for agent execution, allowing developers to create clear security boundaries without sacrificing agent functionality. We’ll explore the fundamental concepts behind Docker-based isolation and demonstrate how sandboxes can serve as a critical layer of LLM agent security for modern AI systems.

Hands-On LLM Agent Security Implementation

Throughout the webinar, participants will build and run a sandboxed LLM agent, assign it practical tasks, and observe how the sandbox environment constrains, monitors, and governs agent actions. As the session progresses, we’ll progressively tighten security policies, introduce permission controls, and demonstrate practical patterns for balancing agent capabilities with operational safety. Participants will gain firsthand experience implementing restrictions and observing how different security controls affect agent behavior while learning practical approaches to LLM agent security.

Real-World LLM Agent Security Patterns

In addition to building the sandbox environment, we’ll discuss common architectural patterns for secure AI agent deployment. Topics will include permission management, resource limitations, monitoring strategies, runtime controls, and governance considerations for agentic systems. We’ll also explore real-world scenarios where sandboxing can help organizations safely deploy AI agents while maintaining security and compliance requirements. Docker Sandboxes have emerged as one of the most effective solutions for LLM agent security because they allow developers to isolate agent actions while maintaining flexibility and performance.

Building Production-Ready LLM Agent Security

By the end of the session, attendees will have a working sandboxed agent environment, a clear understanding of Docker-based AI security patterns, and a practical framework they can immediately apply to their own AI agent projects and LLM-powered applications. Whether you are building experimental agent prototypes or preparing production-grade AI systems, the techniques covered in this session will provide a strong foundation for implementing LLM agent security in real-world environments.

What We Will Cover:

• Why LLM agent security matters in modern AI applications
• Understanding the risks of autonomous agents, tool use, and code execution
• Introduction to Docker Sandboxes (SBX) and container-based isolation
• Creating a secure runtime environment for LLM agents
• Running agents that can perform actions safely within sandboxed boundaries
• Managing file access, tool permissions, and resource constraints
• Monitoring agent behavior inside isolated environments
• Applying security policies and restricting agent capabilities
• Core principles and best practices for LLM agent security
• Best practices for deploying secure AI agents in development and production environments
• Common design patterns for safe agent execution
• Balancing agent autonomy with security and governance requirements
• Real-world use cases for sandboxed AI agents

Hands-On Exercise:

Participants will follow along live as they create a Docker Sandbox environment from scratch and deploy an LLM agent within it. Together, we’ll assign the agent practical tasks involving tool usage, file operations, code execution, and external interactions while observing how the sandbox environment controls and isolates agent behavior.

In the second half of the workshop, attendees will progressively strengthen security controls by adjusting policies, permissions, and runtime restrictions. They will learn how to limit agent capabilities, enforce execution boundaries, and monitor activity within the sandbox. The exercises are specifically designed to demonstrate practical LLM agent security techniques that can be immediately applied to production AI systems. By the end of the session, participants will leave with a functioning sandboxed agent setup and a repeatable architecture they can adapt to secure their own AI agent workflows.

Who Should Attend:

• AI engineers building agent-based applications
• Developers working with LLMs and autonomous AI systems
• Software engineers interested in AI infrastructure and deployment
• Platform engineers responsible for AI application security
• Technical architects designing agentic AI systems
• Machine learning practitioners deploying AI-powered workflows
• Security professionals evaluating AI agent risks
• DevOps engineers supporting AI workloads and deployments
• Anyone interested in secure AI development and agent execution environments

Join us for this hands-on workshop and learn how to implement effective LLM agent security practices using Docker Sandboxes, run LLM agents safely, and build secure foundations for the next generation of autonomous AI applications.