Blog — page 30 of 38
Firecracker vs Nabla Containers: two ways to cut kernel risk
Nabla Containers narrow the host kernel attack surface to roughly seven syscalls without a hypervisor. Firecracker takes the hardware-virtualization route instead. Same enemy — the shared host kernel — two very different walls, and only one runs code you didn't build ahead of time.
Long-Running Sandboxes for AI Agents
One-shot sandboxes — run code, return stdout, die — are perfect for a single tool call and wrong for an agent that iterates for an hour. This is the pillar guide to long-running sandboxes: persistent VMs, hibernate/wake, scale-to-zero, and TTL semantics.
Stateful vs Ephemeral AI Agent Sandboxes
Ephemeral means a fresh VM per task and nothing survives — strongest isolation, zero idle cost, amnesia between runs. Stateful means the VM (or its snapshot) survives so the agent keeps its filesystem, deps, and warm context. This is the post about which one you actually want.
Giving AI Agents Persistent Memory & State via microVM Snapshots
When people say they want their agent to 'remember,' they usually mean two different things: facts (a vector DB's job) and the machine it was working on — its filesystem, installed tools, scratch files, warm caches. A microVM snapshot captures that machine, exactly, and hands it back later.
Resuming & Reconnecting AI Agent Sessions
A session is just a sandbox you can look up by id and re-attach to. Hibernate it while idle so it costs ~nothing, wake it on return with full filesystem and RAM intact — and re-establish the network, because a resumed VM's open sockets are dead.
Always-On vs Scale-to-Zero AI Agent Infrastructure
An agent runs for minutes but is idle most of that time. Always-on gives instant responses and a 24/7 idle bill; scale-to-zero gives ~zero idle cost and a wake-latency tax. The honest map of the spectrum — and why snapshot-restore makes scale-to-zero actually viable.
What Is an AI Agent Sandbox?
An LLM emits actions — shell commands, code, tool calls — that no human vetted before they ran. An agent sandbox is the isolated, disposable place those actions run so a mistake or an attack can't reach your host, your data, or another tenant.
How AI Agent Sandboxes Work
An AI agent decides to run some code. The safe move is to not run it on your machine. This is the mechanism of an agent sandbox — how a model's action becomes a command inside a disposable Firecracker microVM and comes back as a result, without ever touching your host kernel.
Why Every AI Agent Needs a Sandbox
The instant your agent can run a shell command it wrote, you are running untrusted code. Not because the model is evil — because no human reviewed it, it can be injected by whatever it reads, and it hallucinates destructive commands with total confidence. That's what a sandbox is for.
AI Agent Runtime Infrastructure: What It Is, How to Choose, How to Self-Host
An AI agent runtime is the isolated computer your agent's tool calls run on. What it is, how it differs from a framework, how the 2026 options compare, what it costs, and how to self-host one on Firecracker.
Sandbox Your AI Agent's Dependency Audit
To audit a dependency tree you have to resolve and install it, and install scripts are arbitrary code execution you invited in. An AI agent running npm install on a stranger's lockfile is a supply-chain incident on a cron. Give each audit its own disposable Firecracker microVM.
Isolating Genomics Pipelines in Per-Job microVMs
A 400GB BAM file and a `while True` in someone's cleanup script are both just Tuesday. Run each user-submitted Nextflow/Snakemake/WDL job in its own Firecracker microVM, so a runaway alignment can't OOM another lab's run and one tenant's PHI is never reachable by another.
Per-Tenant Fraud Rules in Isolated microVMs
When customers upload their own scoring rules and those rules run on every checkout, a shared worker pool means one tenant's rule can read another's transaction features — or pin a core and add latency to everyone. Give each tenant's rule its own Firecracker microVM.
Red-Teaming LLMs in Disposable microVMs
The whole point of red-teaming is to get the model to try dangerous things so you can catch them. That means you are, on purpose, running code designed to escape. Run each trial in a fresh, network-locked, disposable Firecracker microVM — then kill and forget it.
Firecracker virtio-rng and Guest Entropy Explained
A freshly booted microVM barely knows any randomness, and a restored one thinks it already does — which is worse. Here's how Firecracker's virtio-rng device seeds the guest, what the entropy rate limiter is for, and why restoring the same memory snapshot into many guests is a genuine cryptographic footgun.
Firecracker vs unikernels: two ways to shrink a VM
Both make a VM small, but they shrink opposite ends. A unikernel deletes the operating system; Firecracker shrinks the emulator around a normal one. That one choice decides whether you can run code you didn't compile ahead of time.
Firecracker Snapshot Version Compatibility & Cross-Version Restore
A snapshot is a photograph of a machine that only develops correctly in the same darkroom. Firecracker encodes microVM state in a versioned serialization format, and the CPUID it captured must still match the host you restore on — so snapshots are version-coupled artifacts, not portable blobs.
microVM Memory: Balloon vs Hotplug vs Re-Provision
You don't resize a microVM — you compost it and grow a new one. Firecracker deliberately has no memory hotplug, its virtio-balloon only reclaims idle slack, and guest RAM is frozen at snapshot time. Here's how to actually give your microVM more memory.
Best Firecracker Snapshot & Restore Tools (2026)
How teams actually snapshot and restore Firecracker microVMs in 2026 — the raw FC snapshot API, firecracker-go-sdk / firecracker-containerd, Kata, userfaultfd memory streaming, CRIU, and the managed platforms (E2B, Modal, PandaStack) that hide the whole lifecycle — judged by what fits your need, not a leaderboard.
Firecracker vs Apple's Containerization Framework
Apple's answer to "is a container a VM?" is "yes, quietly, one each." Apple Containerization gives every Linux container its own VM on Virtualization.framework — great local dev on Macs. Firecracker is the Linux/KVM microVM built to run thousands of untrusted workloads per host. Different jobs.
Isolating an AI Code-Review Bot That Runs Untrusted PR Code
A forked pull request is a stranger handing you code and asking you to run it as root. If your review bot builds and runs it, give every PR its own throwaway microVM.
Building an autograder that runs student code safely in microVMs
Every semester a student submits `while True: fork()` or tries to read the answer key. One disposable microVM per submission makes that the VM's problem, not your grader's.
A microVM Harness for SWE-bench & terminal-bench
The agent runs git, pytest, pip, and make for real. Give each benchmark task its own microVM, snapshot the prepared state, capture every command it types, and grade the exit code — so task 7's rm -rf never touches task 8.
Per-Tenant Webhook Signing Secrets in microVMs
One shared process holding every customer's webhook signing secret is a group chat your tenants' keys did not consent to join. Give each signature its own microVM.