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All digests tagged Sandboxing
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Code Mode is an alternative architecture for AI agents that uses a programming sandbox instead of a large tool catalog. This approach drastically reduces context bloat, lowers token usage, and enables emergent behavior by allowing agents to compose tools in code.
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Securing autonomous AI agents requires a layered defense-in-depth approach. Key controls include one identity per agent, default deny egress (including DNS), and deterministic brokers for all tool calls.
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Robert Brennan details four strategies for sandboxing AI coding agents: Docker, GitHub Actions, VMs, and Kubernetes. He argues that Kubernetes offers the best isolation and scale for autonomous agents.
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Matt Brockman walks through deliberate sandbox failures—from runaway processes to memory leaks—demonstrating how to manage resource quotas and maintain state in large-scale, isolated compute environments.
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NVIDIA OpenShell 0.1 provides a secure runtime boundary for AI agents, preventing compromised agents from executing unauthorized actions. It allows developers to deploy agents in a sandboxed environment where all components inherit consistent policy, credential, and audit controls.
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Learn how to evaluate AI agent skills using SkillEvaluator. This multi-tier framework checks for security risks (prompt injection, data exfiltration) and measures 'Skill Lift' across five dimensions to ensure reliability in production workflows.
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Formal verification is necessary for GPU kernels because testing fails due to non-determinism and massive input spaces. The solution involves using SMT solvers to prove algorithmic correctness at the PTX level.
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To scale your software factory, move it into agent sandboxes. This provides isolation, scale, and autonomy by implementing a three-tier orchestration system.
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LangSmith Sandboxes provide a secure, isolated computer for agents. Using hardware virtualized microVMs and an O proxy, they enable running untrusted model-generated code at scale while maintaining strict security controls.
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