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A better model can produce better answers, but it does not by itself complete a multi-step task inside an application. An AI agent also needs a runtime: the layer that runs the model loop, dispatches tools, carries state between steps, enforces approvals, provides computing resources when needed, and records what happened.
What does an AI agent runtime do?
A model generates a response or proposes an action. The runtime turns that capability into an application workflow: it decides what happens next, invokes configured tools, sends tool results back to the model, and manages the run until it finishes, fails, or needs human input.
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In OpenAI’s documentation, the Agents SDK runner handles the agent loop and handoffs. A managed Agents API adds provider-managed sessions, orchestration, context compaction, and recovery. A direct Responses API integration gives the application more of the loop and state-handling work to implement. These are different ways to divide operational responsibility, not different names for the model itself.
The runtime’s main responsibilities
- Loop and tool dispatch: Interpret the model’s request for a tool or another step, invoke the configured tool, and route its result into the next model step.
- State: Preserve conversation or session context across steps and, where supported, across tasks.
- Control and handoffs: Determine when to continue, transfer work, request approval, or return a result.
- Execution: Supply a workspace for tasks that need files, commands, dependencies, or other computing resources.
- Operations: Record the run so a team can inspect tool calls, handoffs, guardrails, failures, and recovery.
The runtime does not make model quality irrelevant. The model still affects what the agent understands and proposes; the runtime determines how those proposals are carried out, constrained, and observed in the application.
Which approach should run the agent: a managed API, an SDK, or direct API calls?
OpenAI’s overview describes three approaches: a managed Agents API, an application-run Agents SDK, and direct Responses API calls. The practical comparison is who owns the work around the model. The table reflects those options as described in OpenAI’s documentation, not a neutral performance comparison or a claim about every provider.
| Approach | Where the loop runs | State and recovery | Tools and execution | What the application team takes on |
|---|---|---|---|---|
| Managed Agents API | Provider-managed harness and orchestration | Provider-managed sessions, orchestration, context compaction, and recovery | Uses the managed agent surface; the overview distinguishes tool execution and execution environment as design considerations | Less runtime infrastructure to integrate; the team still needs to decide which tools and actions the agent may use |
| Agents SDK | The SDK runs the agent loop in the application’s environment | The application team owns state storage and deployment | The application team owns tool implementations and approval decisions; the SDK invokes configured tools | More control over deployment and policy, with responsibility for the surrounding infrastructure |
| Direct Responses API calls | The application builds and runs more of the interaction loop | The application handles the state needed between calls | The application integrates tool calls and any needed execution environment | More flexibility over the integration, and more orchestration and state handling to implement |
These descriptions are specific to the OpenAI interfaces in its documentation. They do not establish that one approach is faster, safer, or less expensive in every application.
Choose by responsibility, not by label
- Choose a more managed harness when reducing runtime integration and infrastructure work matters more than owning every part of orchestration.
- Choose an SDK when your application should own deployment, tool implementations, state storage, and approval decisions while using a runner for the agent loop.
- Choose direct API calls when you want to implement more of the loop and state handling yourself and can operate that integration reliably.
Before committing, identify who will run the loop, store conversation state, connect tools, set approval policy, provide compute, and inspect or recover failed runs. Those answers reveal the real trade-off: more managed infrastructure can simplify the integration, while an application-run design gives the team more operational ownership.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsWhen does an agent need a sandbox?
A sandbox is useful when an agent needs a workspace rather than just a place to return a response. OpenAI’s sandbox guide describes an isolated Unix-like execution environment that can include a filesystem, shell, packages, mounted data, ports, snapshots, and controlled external access.
Use a workspace for work that has files or commands
- Tasks that read, create, or transform files.
- Work that runs shell commands, installs dependencies, or uses packages.
- Tasks that need mounted data, exposed ports, a preview, or a saved snapshot.
- Work that must preserve a workspace so a later step or resumed run can continue from it.
For a short answer that needs no files, commands, or persistent workspace, a sandbox usually adds an unnecessary execution environment. Session or conversation history is not the same thing as a sandbox filesystem: an agent may retain conversational state without retaining a workspace, or use a workspace whose files have a different lifecycle.
Separate the control plane from the execution plane
In the sandbox guide’s framing, the harness is the control plane around the model: it owns the loop, model calls, tool routing, handoffs, approvals, tracing, recovery, and run state. The sandbox is the execution plane where model-directed work reads and writes files, runs commands, installs dependencies, uses mounted storage, exposes ports, or snapshots state.
Keeping that distinction clear helps limit what the model-directed environment can control. Sensitive duties such as authentication, billing, audit logging, review, and recovery belong in trusted orchestration or other trusted application infrastructure—not simply inside the workspace where the agent runs commands. The right implementation depends on whether the harness is managed or application-run, but the security boundary should be explicit in either case.
How should a runtime handle tools, approvals, and private connections?
Tools are not just prompts to the model; they are capabilities exposed by the application. Decide which tools are available, what each can change, and which actions require approval. Keep sensitive credentials and policy decisions in trusted infrastructure rather than exposing them as unrestricted model-directed execution.
For local or private MCP servers, OpenAI’s integrations guidance assigns the runtime responsibility for the connection, approvals, and network boundaries. Hosted MCP can instead route remote tools through a hosted surface. In either setup, establish the network and approval policy deliberately; a tool connection does not by itself define what actions are safe.
What should a team be able to observe and recover?
A useful runtime should make a run inspectable, not leave the team guessing which step produced an outcome. OpenAI’s integrations and observability guide describes traces that can capture model calls, tool calls and outputs, handoffs, guardrails, and custom spans. That record helps teams understand workflow behavior and investigate failures before or alongside formal evaluations.
Think through recovery as well as logging: if a tool fails, an approval is declined, or a run needs to resume, know where run state lives and which system is responsible for continuing or stopping the work. A trace shows what happened; it is not itself a substitute for state management, authorization, or a recovery policy.
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What should you check before choosing an agent runtime?
- Control: Do you want a provider-managed harness, or should your application own deployment and the loop?
- State: Where will conversation or session history live, and how is resumable work handled? Is workspace state managed separately?
- Tools and network: Who implements connections, limits access, sets network boundaries, and decides when approval is required?
- Execution: Does the task need files, commands, packages, mounted data, ports, previews, or snapshots—or only a response?
- Operations: Can the team inspect model and tool activity, handoffs, guardrails, errors, and recovery actions?
- Integration effort: Which infrastructure is the team prepared to build and operate itself?
For OpenAI’s managed Agents API specifically, the overview reviewed on October 7, 2026 states that data residency is supported only in the United States and that the API does not support Zero Data Retention. It also says that using a self-hosted sandbox does not make the Agents API eligible for Zero Data Retention. These are time-sensitive service-policy details, so confirm the applicable terms and data controls for your account and region before deployment.
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