AI Agent Runtime Architecture: Sandbox-as-Tool vs. Agent-in-Sandbox

AI agents do not all use sandboxes in the same way.

Sometimes the agent runtime lives outside the sandbox and calls the sandbox only when it needs a safe place to run code. Sometimes the agent runtime itself lives inside the sandbox and uses that environment as its local computer.

Those are two different agent runtime shapes:

• sandbox-as-tool
• agent-in-sandbox

They are not competing slogans. They optimize for different workloads.

The mistake is treating one of them as the universal answer. A serious managed-agent platform needs both. Some agents need a resident runtime that can respond immediately and claim sandboxes only for tool execution. Others need the whole agent engine, workspace, dependencies, sidecars, and local processes to live together inside the sandbox.

This distinction is becoming more important as the market shifts from one-shot code execution to production agent infrastructure. Vercel's Open Agents makes the workflow and sandbox split explicit with a Web -> Agent workflow -> Sandbox VM architecture. OpenAI's recent Agents SDK update also moves the SDK toward a model-native harness with native sandbox execution and support for more sandbox providers.

The category is getting clearer: the runtime question is not whether an agent should use a sandbox. The question is whether the sandbox is the agent's host or one of the agent's tools.

What Is an AI Agent Runtime?#

An AI agent runtime is the infrastructure that keeps an agent session useful after the first model call.

It usually owns:

• session lifecycle
• model calls
• tool execution
• streaming events
• cancellation
• recovery
• permissions
• memory and workspace state
• usage and billing boundaries

An agent framework helps you define what the agent can do. A runtime decides how that agent actually runs in production.

For sandboxed agents, the runtime also needs to answer a placement question:

Both engines use sandboxes. They just use them differently.

Pattern 1: Sandbox-as-Tool#

In the sandbox-as-tool model, the agent runtime runs outside the sandbox.

The sandbox is claimed, resumed, or attached only when the agent needs an isolated execution target:

• run a shell command
• execute code
• inspect files
• render a browser page
• run tests
• transform data

Conceptually:

This is the shape used by many code-interpreter-style systems. It is also a natural fit for OpenAI Agents SDK based runtimes where the agent loop can live in a service and call into Sandbox0 only for tool execution.

Why Sandbox-as-Tool Is Fast#

The agent runtime is already warm.

When a user sends a message, the system can validate the request, load session state, call the model, and start streaming without first booting a per-session agent process inside a sandbox.

If the model can answer without running tools, no sandbox needs to be active at all. If the model needs a bash command or file operation, the runtime claims or resumes a sandbox at that moment.

That changes the latency profile:

• text-only turns can respond quickly
• tool turns pay sandbox latency only when tools are needed
• the agent process does not have to be recreated with every sandbox
• the runtime can keep model/provider configuration, session state, and control logic ready outside the execution environment

For interactive products, that matters. The fastest sandbox is still slower than not needing a sandbox for a turn that never executes code.

Why Sandbox-as-Tool Can Cost Less#

Sandbox-as-tool also changes the cost model.

If the sandbox is not the agent host, the sandbox does not have to stay active while:

• the model is thinking
• the user is reading
• the session is idle
• the runtime is deciding whether a tool call is needed
• the agent is producing a plain text answer

The sandbox active window can shrink to the actual execution window.

That is the main economic advantage of sandbox-as-tool. It lets the runtime stay available while keeping expensive isolated execution resources active for less time.

In Sandbox0 Managed Agents, billing is based on session running time, not on which engine shape the session uses. The same pricing boundary applies whether the session is backed by an agent-in-sandbox engine or a sandbox-as-tool engine. That keeps the product model simple: users pay for managed agent sessions, while the platform is free to choose the right execution strategy under the hood.

Internally, sandbox-as-tool gives the platform more room to optimize active sandbox time. A resident runtime can keep session control alive while using sandboxes only for execution-heavy turns.

When Sandbox-as-Tool Fits Best#

Sandbox-as-tool is a strong fit when:

• many turns are conversational or planning-heavy
• tool calls are bursty
• low first-response latency matters
• the agent engine can run safely outside the sandbox
• the sandbox should mainly provide isolation for execution
• cost is sensitive to sandbox active time

Good examples include:

• data analysis agents
• short command execution agents
• support or ops agents that occasionally need shell access
• agents that call multiple execution sandboxes in parallel
• OpenAI Agents SDK runtimes with sandbox-backed tools

Pattern 2: Agent-in-Sandbox#

In the agent-in-sandbox model, the agent runtime lives inside the sandbox.

The sandbox is not just a tool. It is the agent's host environment.

Conceptually:

This is the right shape for runtimes that expect a real local machine.

Examples include:

• Claude Code or Claude Agent SDK style runtimes
• Codex app-server style runtimes
• self-hosted coding agents that keep local process state
• agent servers with local MCP servers, sidecars, or long-lived helper processes

In this model, the agent is close to its workspace. It can share local files, installed packages, dev servers, terminal state, and supporting processes with the environment it is modifying.

Why Agent-in-Sandbox Still Matters#

Sandbox-as-tool is efficient, but it is not always enough.

Some agent engines are built around the assumption that the agent owns a local development environment. Moving only tool calls into a remote sandbox can break that assumption.

Agent-in-sandbox is useful when the agent needs:

• a persistent local workspace
• installed dependencies
• long-running dev servers
• local MCP servers
• sidecar processes
• private network access scoped to that sandbox
• runtime state that should be isolated with the execution environment

This shape is closer to how a human developer works in a project directory. The agent is not sending disconnected commands to a remote executor. It is living in the environment where the work happens.

That matters for coding agents that need to repeatedly inspect files, run tests, edit code, start services, and use local tools over a long session.

The Cost Tradeoff#

Agent-in-sandbox can have a higher resource floor.

If every active session keeps an agent runtime and its supporting processes inside a sandbox, then the sandbox may stay active for more of the session. The platform has to care about warm pools, memory footprint, sidecar readiness, idle cleanup, pause and resume, and workspace persistence.

That does not make the model wrong. It means the infrastructure has to treat agent runtime cost as a first-class concern.

For Sandbox0 Managed Agents, the user-facing pricing boundary stays the same: the session is billed by running time regardless of engine shape. The platform can still optimize internally:

• warm the right templates
• pause idle sandboxes
• preserve workspace state in volumes
• resume sessions without losing files
• move compatible workloads to sandbox-as-tool engines
• keep agent-in-sandbox for engines that need full local environment semantics

The right answer is workload-dependent, not ideological.

Comparing the Two Runtime Shapes#

The important point is that both are runtime engines. Both can be valid. Both need sandbox infrastructure.

Why Pricing Should Follow Session Semantics#

Pricing can accidentally force architecture.

If a platform bills by raw sandbox lifetime, users start thinking about every runtime placement decision as a sandbox meter problem. That makes the product harder to reason about, especially when the platform supports more than one engine shape.

Managed agents are better priced around the managed session.

The user is buying:

• a durable session
• event history
• runtime orchestration
• model/tool execution
• recovery behavior
• sandbox-backed isolation
• managed lifecycle semantics

Whether the engine is sandbox-as-tool or agent-in-sandbox should be an implementation choice unless the user explicitly needs to choose for compatibility or isolation reasons.

That is the approach Sandbox0 Managed Agents takes: sessions are charged by session running time across both engine shapes.

This keeps the external model stable while allowing the platform to optimize internally:

• sandbox-as-tool can reduce active sandbox windows
• agent-in-sandbox can preserve local runtime compatibility
• both can share the same managed-agent API and event model
• users do not need to learn a different billing unit for every engine

The result is a cleaner abstraction: choose the engine for technical fit, not because the pricing model punishes one architecture.

How Sandbox0 Supports Both#

Sandbox0's managed-agent architecture separates the public session model from the engine implementation underneath it.

At the API layer, the system exposes managed-agent objects:

• agents
• environments
• vaults
• credentials
• sessions
• session events

Below that, engine routing decides how the session should run.

For agent-in-sandbox engines, Sandbox0 can claim a sandbox from a managed template, mount workspace volumes, expose the runtime through a controlled endpoint, and keep the agent process close to the files, tools, and sidecars it needs.

For sandbox-as-tool engines, Sandbox0 can run a resident agent runtime outside the per-session sandbox. The runtime keeps the agent loop and session control active, then claims or resumes a Sandbox0 sandbox only when a tool needs isolated execution.

That gives us this shape:

The sandbox layer does not force one agent architecture. It gives both architectures the primitives they need:

• fast sandbox claim
• persistent volumes
• snapshot, restore, and fork
• controlled network policy
• egress auth
• workspace mounts
• runtime webhooks
• warm templates

That is the more durable product boundary. Agent engines will keep changing. The execution substrate should support more than one of them.

The Practical Rule#

Use sandbox-as-tool when you want fast interaction, shorter sandbox active time, and efficient execution for bursty tool calls.

Use agent-in-sandbox when the agent engine needs to live with the workspace, dependencies, sidecars, and local process state.

Use a managed-agent platform when you need both under one session model.

That is where the market is going. The future of agent infrastructure is not one sandbox pattern winning forever. It is a runtime layer that can route sessions to the right engine shape without changing the API, event model, or pricing boundary users depend on.