SandBase: The Agent-native Platform for Production AI Agents

SandBase is a comprehensive Agent-native Platform designed for builders developing and deploying production-grade AI agents. It provides an integrated runtime environment that consolidates essential services like model calls, secure sandboxes, tool access, and session management, moving beyond one-off chat demos to robust agent applications.

It targets backend teams, AI coding workflows, product teams, and platform teams seeking to streamline their AI agent development lifecycle.

Key Features:

• Model Routing: Call leading AI providers (OpenAI, Anthropic, Google, DeepSeek, Meta) through consistent schemas, with fallback mechanisms and per-request cost tracking.
• Secure Cloud Sandboxes: Execute generated code, file operations, and browser-like workflows in isolated environments, ensuring security and preserving outputs.
• MCP Servers & Skills: Provide agents with governed access to tools, data sources, and reusable instructions, enabling consistent capabilities across various agent types.
• Durable Sessions: Store agent messages, tool calls, traces, files, and artifacts for observability, debugging, replay, and billing.
• OpenAI-Compatible API: Access the platform through a familiar API, a terminal CLI, and MCP connectors for seamless integration.
• Agent-Native Design: Built specifically for complex agent applications, prioritizing runtime concerns like security, cost, and observability.

Use Cases:

SandBase is invaluable for scenarios where AI agents need to perform real work. For instance, a customer support agent can retrieve account data, interact with language models, generate and execute remediation scripts in a secure sandbox, and save the entire interaction history. A research agent can collect and summarize sources using routed models, enrich data with MCP tools, and expose every step for traceability.

Furthermore, coding agents can leverage SandBase's builder guide, CLI, and MCP setup to understand how to correctly use endpoints, sandboxes, and models, facilitating automated code generation, testing, and file management within isolated environments. These capabilities ensure a consistent developer experience from local testing to production deployment and ongoing operational review.

Pricing Information:

SandBase offers a flexible pricing model, allowing users to get started for free. Detailed pricing plans are available on their website, catering to various team sizes and usage requirements.

User Experience and Support:

The platform provides multiple access points including an OpenAI-compatible API, a terminal CLI, and a console for managing API keys and monitoring usage. Comprehensive documentation, including a 'builder.md' guide, is available to assist developers. The dashboard offers visibility into traces, session events, generated artifacts, usage, and cost, ensuring transparent operational review.

Technical Details:

SandBase's backend is designed to route requests across diverse AI models (language, vision, image, video, audio). It integrates secure cloud sandboxes for isolated execution and offers both an OpenAI-compatible endpoint and SandBase-specific agent and runtime APIs for advanced workflows requiring sessions, tools, and richer outputs.

Pros:

• Comprehensive, integrated platform for production AI agents.
• Reduces the complexity of stitching together disparate services.
• Enhanced observability, debugging, and cost tracking for agent workflows.
• Secure and isolated execution environments with cloud sandboxes.
• Flexible integration options via API, CLI, and MCP connectors.
• Supports gradual adoption from simple model calls to full agent loops.
• Consistent developer experience from prototyping to production.

Cons:

• Potential learning curve for SandBase-specific agent and runtime APIs.
• Reliance on a single platform for multiple critical agent components.
• No explicit mention of open-source components or self-hosting options.

Conclusion:

SandBase stands out as an essential Agent-native Platform for teams looking to build, deploy, and manage sophisticated AI agents in production. By consolidating critical runtime services and offering robust controls for security, cost, and observability, it empowers builders to move beyond experimental demos to reliable, scalable AI applications. Get started free today to explore how SandBase can transform your agent development.