As developers increasingly connect Large Language Models to enterprise tools, executing non-deterministic AI workflows introduces severe security vulnerabilities. Granting autonomous agents unfettered access to infrastructure via systems like the Model Context Protocol (MCP) creates unacceptable supply chain risks and potential state corruption if not strictly isolated.

This session demonstrates how to mitigate these risks by wrapping agentic workflows in ephemeral Docker and E2B sandboxes. Rather than trusting the AI model, engineers must trust the boundaries. The presentation will walk through a live, production-ready architecture where an LLM securely interacts with a GitHub MCP server entirely within an isolated container environment. >
Attendees will learn to architect zero-trust boundaries around AI agents, configure MCP gateways directly within Docker sandboxes, and safely automate external systems without exposing host infrastructure. By wrapping non-deterministic AI in highly deterministic infrastructure, teams can scale their AI-native transformations without compromising platform integrity.