Abhishek Bhardwaj traces the isolation options available for code-running agents, from ordinary processes and containers through gVisor-style boundaries and microVMs. Each choice trades startup speed, compatibility and security against operational complexity.
A useful sandboxAn AI agent sandbox is an isolated execution environment that limits which files, processes, networks, credentials, and external systems an agent can access. also needs persistence. Snapshots and restore pointsCheckpointing saves a defined execution state so a computation or workflow can resume or recover from that point. let long-running agents recover state, branch experiments and support reinforcement-learningReinforcement learning trains an AI system to choose actions using feedback about the results of its earlier choices. workloads without rebuilding the environment after every step.
The final layer is orchestrationAgent orchestration coordinates AI agents, tools, people, tasks, state, and control flow so a larger workflow reaches a verified outcome. across a fleet: selecting nodes, controlling networking, exposing approved ports and managing lifecycle events at low latency. Bhardwaj's architecture shows why an agent sandbox is a distributed systems product, not merely a container wrapper.
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