An LLM that's 99% accurate on every step still fails more than 60% of the time on a 100-step autonomous workflow. That's not a hypothetical; it's math. Long chains amplify tiny errors, causing reliability to collapse exponentially. Containment, not model intelligence, is what actually bounds the damage when an AI agent goes off the rails. NemoClaw Sandboxing Instructions is a hands-on, cross-platform blueprint for running autonomous AI agents inside hardened, disposable containment boundaries on macOS, Linux, and Windows. Every command, every policy example, and every incident in this book was captured against a real, running system, including live troubleshooting logs, real crash-loop recoveries, and real red-team findings. Build least-privilege network policy, isolate the filesystem, keep credentials out of the sandbox entirely, and know exactly what your agent did, not what you assumed it did. This Second Edition is updated and expanded for the current NemoClaw, OpenShell, and Hermes stack, with a corrected page count and refreshed content throughout.
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