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Neither OpenAI nor Anthropic is a universal winner for building AI agents. The key difference is how much of the agent runtime each platform manages—and where your application must take over. OpenAI documents a managed Agents API, an application-run Agents SDK, and the lower-level Responses API. Anthropic documents Managed Agents and a tool system that separates tools run on Anthropic’s infrastructure from tools your application executes. Choose based on who should own deployment, state, tool execution, approvals, and the execution environment.
How do OpenAI and Anthropic divide responsibility for an agent?
Think of an agent as more than a model call: it has a loop that interprets results, chooses or requests tools, handles state, and decides what happens next. A managed runtime takes on more of that orchestration. An application-run SDK or custom loop leaves more responsibility—and control—with your team.
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OpenAI’s Agents API, Agents SDK, and Responses API are different integration paths, not three names for the same setup. Anthropic’s Managed Agents provide a bundled agent configuration, while Anthropic’s tool reference separately identifies which tools run on its infrastructure and which need client-side execution. The comparison below reflects the official documentation reviewed on October 7, 2026; product availability and tool identifiers can change.
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|---|---|---|
| Runtime ownership | Agents API: managed harness. Agents SDK: runs in your application, where the SDK runs the agent loop and invokes tools. Responses API: direct model calls or a foundation for an agent you build yourself. (OpenAI Agents overview and Agents SDK guide.) | Managed Agents bundle a model, prompt, tools, MCP servers, and skills into an agent configuration. Anthropic’s tool reference also describes client-executed tools, for which your application handles execution. (Anthropic Managed Agents setup and tool reference.) |
| State and sessions | The Agents API overview describes saved session state. With the SDK, your application owns state storage. The Responses API is the lower-level path; the overview does not establish an equivalent managed-session arrangement for every custom implementation. (OpenAI Agents overview and Agents SDK guide.) | The Managed Agents setup describes the agent configuration, but the cited documentation does not establish a directly comparable state-persistence or session model. Check the current API and product documentation for the surface you plan to use. (Anthropic Managed Agents setup.) |
| Who executes tools? | Tool options include built-in tools, function calling, programmatic tool calling, tool search, and remote MCP servers. The configuration point depends on whether you choose the API, an Agents API agent, or an SDK agent definition. (OpenAI tools documentation.) | Server tools execute on Anthropic’s infrastructure; client tools are executed by your application. Documented tool categories include web search, web fetch, code execution, MCP, and computer use. (Anthropic tool reference.) |
| Execution environment | The managed Agents API provides a managed harness. With the Agents SDK, your application owns deployment and tool implementations; the Responses API lets you build the orchestration yourself. The chosen path affects how much of the environment is provider-managed. (OpenAI Agents overview and Agents SDK guide.) | Server tools run on Anthropic’s infrastructure, while client tools run in the application’s environment. For computer use, the application controls the environment and runs the interaction loop: it executes requested actions and returns results. (Anthropic tool reference and computer-use documentation.) |
| Integration effort and control | The overview distinguishes the managed Agents API, the application-run SDK, and the lower-level Responses API by integration effort and control. In the SDK path, the application owns deployment, tool implementations, storage, and approval decisions. (OpenAI Agents overview and Agents SDK guide.) | Managed Agents bundle several parts of an agent configuration. Client-side tools leave execution with the application. The cited Anthropic materials do not establish a directly comparable, complete control-versus-effort matrix across its surfaces. (Anthropic Managed Agents setup and tool reference.) |
| Observability | The Agents SDK’s server-side tracing is enabled by default in the documented normal path and can record model calls, tool calls and outputs, handoffs, guardrails, and custom spans. This describes documented trace contents, not comparative performance. (OpenAI Agents SDK observability documentation.) | The Anthropic materials cited here do not establish like-for-like trace retention, evaluation, or observability capabilities. Compare the current documentation for the particular runtime you intend to use before treating the platforms as equivalent on this point. |
| MCP and compatibility | Remote MCP servers are among the documented tool options. Available configuration depends on the API or runtime surface selected. Verify supported combinations for your implementation. (OpenAI tools documentation.) | Anthropic documents MCP use across the Messages API, Claude Code, Claude.ai, and Claude Desktop; this breadth does not mean integrations behave identically. Verify the current model and tool-version combination, especially for computer use. (Anthropic MCP documentation and tool reference.) |
Which OpenAI agent path should you choose?
Choose the Agents API when you want a managed harness
OpenAI describes the Agents API as a managed Codex harness with saved session state and infrastructure management. This is the option to investigate when you want the provider to take on more of the runtime rather than assembling the loop and its supporting infrastructure in your application. Confirm that its current capabilities, availability, and controls fit your deployment before committing.
#1 Best Overall
Choose the Agents SDK when your application should own the runtime boundary
The Agents SDK runs in your application. The SDK runs the agent loop and invokes tools, while your application owns deployment, tool implementations, state storage, and approval decisions. This separation is useful when those responsibilities need to remain under your service’s control, even though the SDK supplies orchestration code.
Choose the Responses API for direct calls or a custom agent
The Responses API is the lower-level option for direct model calls or building an agent from scratch. It gives your team room to design its own orchestration, but the application must account for the runtime responsibilities that a managed harness would otherwise handle. Do not assume state handling or tool execution is managed just because a model API supports tools.
What does Anthropic manage, and what must your application run?
Managed Agents bundle an agent configuration
Anthropic’s Managed Agents setup describes an agent as a bundle of a model, system prompt, tools, MCP servers, and skills. That is a managed configuration path; it should not be treated as proof that every runtime, state, or deployment responsibility matches OpenAI’s managed Agents API. Check the current Managed Agents status and availability for your region and use case.
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Server tools and client tools have different execution boundaries
Anthropic’s tool reference distinguishes server tools that execute on Anthropic’s infrastructure from client tools that your application executes. That distinction is operationally important: with a client tool, your service needs to receive the request, perform the action, and return the result. The tool list includes web search, web fetch, code execution, MCP, and computer-use capabilities, but the presence of a tool category alone does not tell you which runtime is responsible for the full workflow.
Rank #3
Computer use requires an application-controlled interaction loop
For computer use, Claude requests actions and the application executes them in an environment it controls, then returns the results. The application therefore remains part of the execution loop; this is not simply a provider-run agent operating an unspecified desktop on your behalf. Anthropic’s documented tool versions and supported model combinations are version-sensitive, so validate the exact pairing before implementation.
How should you compare state, tools, and observability?
Start with the session and state model
OpenAI’s overview explicitly describes saved session state for the managed Agents API, while the SDK guide places state storage with the application. For Anthropic Managed Agents, the cited setup documentation describes the agent bundle but does not establish a comparable persistence model. If an agent must resume work, survive restarts, or share state across services, test those exact lifecycle requirements rather than inferring them from the word “managed.”
Map every tool to its executor
For each tool, write down whether the provider or your application performs the actual action, what permissions it needs, and how results return to the agent. OpenAI’s available tools and configuration points vary by runtime surface. Anthropic makes the server-tool versus client-tool boundary explicit. With either provider, check current tool identifiers and supported model combinations; compatibility can change.
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OpenAI’s Agents SDK documentation names trace events including model calls, tool calls and outputs, handoffs, guardrails, and custom spans. The Anthropic materials cited here do not establish an equivalent trace-retention or evaluation comparison. That is a limit on what can be concluded from these sources, not evidence that Anthropic has no observability features. Inspect the documentation for the specific surfaces you plan to use and test whether their traces answer your operational and audit questions.
Best Value
How do you decide which platform fits your agent?
Choose by assigning responsibilities, not by awarding a single capability score. For a practical proof of concept, implement one representative workflow with the same tools, approval rules, state needs, and deployment constraints you expect in production.
- Set the runtime boundary. Decide whether you want a provider-managed harness, an SDK running in your service, or a custom loop. Map that choice to the specific OpenAI or Anthropic surface under consideration.
- List the application-owned work. Identify who deploys the agent, stores state, implements tools, approves consequential actions, and operates any computer or sandbox environment.
- Exercise a real tool cycle. Include a tool request, execution, returned result, and a failure case. Confirm where execution happens and how the application handles an error or an approval requirement.
- Test the lifecycle. Check whether the agent can resume or continue as required after a restart, timeout, or handoff. Do not assume session behavior is the same across APIs or providers.
- Inspect the operational evidence. Verify that available traces and logs capture the events your team needs to debug and review. Compare concrete documented behavior, not presumed feature parity.
- Recheck current compatibility and availability. Confirm the Managed Agents status, API or SDK configuration, tool identifiers, model support, and data controls for the exact product surface and deployment you plan to use.
The winning proof of concept is the one that satisfies your requirements with an ownership boundary your team can operate. The available official documentation supports an architectural comparison, not a universal winner or an apples-to-apples claim about model performance on your workload.
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