One of the things that separates toy agent demos from production deployments is how you handle code execution. The demo runs Python in a subprocess. The production system has a thousand users running agents simultaneously, and you need each execution isolated, resource-limited, and recoverable if it goes sideways.

The good news is that 2026 has reasonably mature options for this. The less-good news is that the tradeoffs between them are real, and the right choice depends on what your agents are actually doing.

Why You Can’t Just Run Code Locally

If your AI agent is writing and executing code — which is increasingly common in data analysis agents, coding assistants, and any workflow that involves transforming data — running that code in your application’s process is asking for trouble. An agent that generates import subprocess; subprocess.run(["rm", "-rf", "/"]) (or anything in between) needs to be running somewhere it can’t hurt anything.

The risk isn’t just malicious code. It’s resource exhaustion. It’s infinite loops. It’s an agent that works fine in testing and then encounters an edge case in production that causes it to spawn processes that eat all available memory. You want execution environments that are isolated, resource-capped, and disposable.

There’s also the practical benefit for the agents themselves: sandboxed execution enables iterative debugging. The agent runs code, observes the error, revises, and runs again — all without risk to the surrounding system. This iteration loop is a large part of why code-capable agents are so much more capable than ones that can only generate code without running it.

E2B: Purpose-Built for AI Agents

E2B is the most widely adopted option among teams building agentic systems specifically. It provides micro-VMs that spin up in roughly 200ms — fast enough to be transparent in most agentic workflows — with persistent filesystems, network access, and pre-installed environments for Python, Node.js, and bash.

The core abstraction is the Sandbox:

from e2b_code_interpreter import Sandbox

with Sandbox() as sandbox:
    # Run code the agent generated
    execution = sandbox.run_code("import pandas as pd; df = pd.DataFrame({'x': [1,2,3]}); print(df)")
    print(execution.text)
    # Upload/download files, install packages, persist state between calls

E2B’s design choices reflect that it was built for agents rather than adapted from a general-purpose cloud platform. Each sandbox persists state across multiple code executions within a session, so an agent can install a library, then use it, then run analysis on the output — all within the same environment without re-initialising. This stateful execution model is much more natural for iterative agentic workflows than fresh-environment approaches.

The SDK handles streaming output, so you can display execution results to users in real-time rather than waiting for a full execution to complete. That matters for long-running data analysis tasks where intermediate progress is meaningful.

E2B’s pricing is per compute-second, with a free tier that’s adequate for development and low-volume production.

Modal is a serverless compute platform that gained traction for ML workloads and has become a solid option for agent code execution when those workloads involve GPU-accelerated libraries or complex Python dependency chains.

Where E2B is optimised for fast spin-up and general-purpose execution, Modal is optimised for workloads that need pre-built images with heavy dependencies (PyTorch, CUDA, scientific computing libraries) that would take minutes to install from scratch.

import modal

app = modal.App("agent-execution")

@app.function(
    image=modal.Image.debian_slim().pip_install("pandas", "numpy", "scikit-learn"),
    timeout=60
)
def run_agent_code(code: str) -> dict:
    import io
    import contextlib
    
    output_buffer = io.StringIO()
    with contextlib.redirect_stdout(output_buffer):
        exec(code, {})
    return {"output": output_buffer.getvalue()}

Modal’s container images are built once and cached aggressively, so the first call might take 30 seconds (image pull + cold start) but subsequent calls within the same session are much faster. That cold start behaviour makes Modal less ideal for latency-sensitive interactive agents, but it’s excellent for batch processing, scheduled analysis, and any workflow where the first-run cost can be amortised.

Daytona: Full Dev Environments for Code Agents

Daytona takes a different angle: instead of lightweight execution sandboxes, it provides full development environment workspaces that persist across multiple agent interactions. You spin up a workspace, and the agent can interact with it repeatedly — writing files, running tests, checking git status, running a dev server — as if it were working in a real development environment.

This is the right model for coding agents that are doing meaningful software development work rather than one-shot code execution. If your agent is iterating on a codebase, the stateful workspace approach is much more natural than re-creating an execution environment on each invocation.

Daytona is more infrastructure overhead than E2B or Modal — you’re managing workspaces rather than invoking functions — but it’s the approach that tools like Devin and similar software development agents use for persistent coding sessions.

Which to Choose

The decision comes down to your workload:

E2B is the default choice for most agent code execution. Fast spin-up, simple API, designed specifically for agents, handles the common cases well. If you’re building a data analysis agent, a coding assistant, or any agent that needs to execute generated code, start with E2B.

Modal makes sense when your agents are running ML inference, working with large data libraries, or need GPU access. The dependency management is excellent and the scaling behaviour suits high-volume production workloads. Worth the additional setup cost if your workloads need it.

Daytona is for agents doing sustained software development work rather than one-off execution. If your agent needs to understand and modify a codebase over multiple interactions, a persistent workspace is the right abstraction.

Worth noting: several agentic frameworks now have native integrations with these services. LangGraph Cloud uses Modal under the hood for certain execution contexts. Claude Code’s architecture is informed by similar sandbox thinking. If you’re choosing a framework first, check what execution infrastructure it already supports before adding another dependency.