Agent Sandbox

Product Introduction

1. Definition: Agent Sandbox is a secure sandboxed computing environment (technical category: AI agent infrastructure) that provides isolated cloud-based compute resources, storage, and artifact management for autonomous AI agents.
2. Core Value Proposition: It solves the agent execution gap by enabling AI agents to securely run Python/Bash code, install packages, process files, and generate deliverables (charts, PDFs, datasets) without compromising local systems, making AI agents operationally capable rather than just intelligent.

Main Features

1. Secure Code Execution:

   • How it works: Uses container-based sandboxing (likely Docker/Linux namespaces) to isolate untrusted code. Agents execute Python/Bash scripts with resource constraints (e.g., 256MB RAM, 30s timeout).
   • Tech stack: Real-time output streaming, Python 3.11 runtime, and kernel-level security controls.

2. Automated Dependency Management:

   • How it works: Agents declare required libraries (e.g., pandas, matplotlib) in manifests. The system auto-installs dependencies via pip/apt in ephemeral environments, ensuring reproducibility.
   • Tech stack: Dynamic package resolution and version locking during runtime initialization.

3. Unified Artifact Pipeline:

   • How it works: Supports file uploads (e.g., CSV datasets) and automated retrieval of outputs (charts, logs, transformed data) via cloud storage. Files persist per-session with encryption.
   • Tech stack: S3-compatible storage ($0.0005/MB), REST API for artifact upload/download.

4. Framework-Agnostic Integration:

   • How it works: SDK (agentsandbox-sdk) plugs into AI agent frameworks (Claude, Codex, Cursor) via API keys. Works client/server-side with <5min setup.
   • Tech stack: Python SDK, WebSocket-based session management, OAuth for Google sign-in.

Problems Solved

1. Pain Point: Eliminates manual intervention for AI-generated code execution. Solves security risks from running untrusted agent code locally and workflow fragmentation from disconnected tools.
2. Target Audience:
   • AI developers building code-writing agents (e.g., LangChain/AutoGPT engineers)
   • Data teams creating AI analysts for automated reporting
   • SaaS platforms needing embedded code interpreters
3. Use Cases:
   • Automated data analysis (Python scripts → visualizations)
   • Agent workflow testing with real code execution
   • Secure document processing pipelines (PDF → transformed datasets)

Unique Advantages

1. Differentiation: Unlike pure "AI playgrounds," Agent Sandbox combines sandboxed compute, dependency resolution, and artifact management in one API. Competitors lack integrated file handling/auto-install features.
2. Key Innovation: Manifest-driven environment provisioning – agents declare needs (libraries/tools), and the system builds environments on-demand without pre-configuration.

Frequently Asked Questions (FAQ)

1. How does Agent Sandbox ensure code execution security?
   Uses hardware-isolated containers, read-only filesystems, and strict resource quotas to prevent system access or persistent attacks.

2. Can Agent Sandbox handle large datasets for AI data analysis?
   Yes, it supports CSV/uploads up to GB-scale (storage billed at $0.0005/MB) and integrates pandas/NumPy for dataframe processing.

3. What AI frameworks integrate with Agent Sandbox?
   Compatible with Claude, Codex, Cursor, Windsurf, and any framework using Python SDK/REST API calls.

4. Is there a free tier for testing Agent Sandbox?
   Yes, $10 free credits (≈11 compute hours) with Google sign-in, no commitment required.

5. How are dependencies managed for custom Python scripts?
   Agents specify libraries in manifests; sandbox auto-installs them via pip before execution, ensuring version consistency.