OpenAI may use DevDay 2026 to introduce Managed Agents, a new platform layer for building, running, and deploying AI agents. TestingCatalog’s September 7 report says unreleased OpenAI Platform code references Agents, Environments, and Agent Sessions, along with support for OpenAI-hosted and customer-managed runtimes.
The reported interface also connects agent creation with a Codex-powered developer plugin in ChatGPT. If the product ships as shown, a user could describe an agent, configure its tools and execution environment, then deploy it without manually assembling every part of the infrastructure.
This remains pre-release evidence rather than a confirmed OpenAI announcement. As of September 7, 2026, OpenAI had not published Managed Agents documentation, pricing, availability details, or a public API specification. The screenshots nevertheless point to a larger shift: OpenAI may be preparing to sell not just models and agent-development components, but the operational control plane needed to keep agents running.
The Report Points to Agents, Environments, and Sessions
TestingCatalog says the unreleased platform separates Managed Agents into three main resource types: Agents, Environments, and Agent Sessions. One screenshot shows a beta POST /v1/agents endpoint and a creation form containing a model, instructions, reasoning settings, output configuration, tools, skills, and plugins.
Another reported screen presents a four-stage workflow:
- Define the agent, including its model, instructions, and optional tools.
- Choose an OpenAI-hosted runtime or attach a customer-controlled runtime.
- Create a persistent session with optional initial input.
- Exchange events with the running agent.
The interface describes a session as a durable workspace, suggesting that an agent could maintain task state across multiple interactions rather than starting from an empty context on every request.
OpenAI has not explained the formal boundaries between these resources, but the likely division is straightforward:
- An Agent would contain the reusable definition, such as its model, instructions, tools, skills, and behavior.
- An Environment would define where the agent executes and which compute, files, credentials, network connections, and software packages it can access.
- An Agent Session would represent one continuing task or interaction, including its history, state, files, and runtime events.
That separation matters operationally. Developers could update an agent definition without treating every running session as the same object. They could also operate several agents inside controlled environments while applying different permissions, resource limits, or compliance policies.
The names and relationships could still change before release. Pre-launch code often contains experiments, placeholder terminology, and incomplete interfaces. The screenshots are better treated as evidence of OpenAI’s direction than as final API documentation.
Managed Agents Would Package Existing OpenAI Building Blocks
OpenAI already offers most of the individual components needed to create an agent. AgentKit includes Agent Builder, ChatKit, connectors, evaluation tools, and reinforcement fine-tuning. OpenAI introduced that collection at DevDay 2025 as a way to move agent projects from prototypes toward production deployments.
The current Agent Builder documentation describes a visual canvas for assembling multi-step workflows. Developers can publish a workflow, embed it through ChatKit, or export code for use with the Agents SDK. Agent Builder therefore helps define the workflow, but developers still need to decide how the surrounding application, runtime, credentials, storage, and deployment process will work.
Managed Agents appears to sit one level above that toolchain. Instead of publishing a workflow and separately building the system that operates it, developers could create a first-class agent resource and attach it to a managed execution environment.
OpenAI has also been preparing the technical foundation. In June 2026, the company moved the Agents SDK runtime onto the Responses API and added unified shell and computer tools, approval policies, guardrails, compaction, and support for multiple compute providers. OpenAI said the revised architecture separates the agent harness from the compute layer, allowing developers to connect it to different infrastructure.
The SDK already supports persistent sessions that automatically retrieve and store conversation history. Developers can use local memory, SQLite, OpenAI-hosted conversations, or custom backends. A Managed Agent Session could promote that concept from an SDK feature into a platform resource with its own identifier, lifecycle, event stream, access policies, and monitoring. That interpretation is plausible, but OpenAI has not confirmed the leaked resource’s exact behavior.
In practical terms, OpenAI appears to be moving from “here are the pieces for an agent” toward “here is the service that operates the agent.”
The Codex Plugin Could Lower the Setup Cost
TestingCatalog describes a plugin that would help users build, configure, and deploy Managed Agents. The screenshot itself labels the product OpenAI Developer Plugin, rather than simply Codex Plugin, and shows it as a ChatGPT app connected to OpenAI Codex.
OpenAI’s current Developer Plugin documentation says the tool can produce deployable code through natural-language instructions in ChatGPT. Its documented workflows include connectors, Apps SDK applications, websites, landing pages, and general coding projects. Users describe the project, review a plan, let Codex generate and validate the code, then deploy or download the result.
Integrating that workflow with Managed Agents could remove several setup steps. A user might describe an internal research agent, allow the plugin to generate its instructions and tool definitions, select an execution environment, add approved connectors, and create a deployment from the same ChatGPT conversation.
That would lower the initial engineering cost, but it would not eliminate the difficult work. A generated agent still needs accurate tool schemas, scoped credentials, realistic evaluations, recovery logic, cost controls, and rules for handling ambiguous or unsafe actions. Making creation conversational can accelerate a prototype. It cannot prove that the resulting agent is safe to operate without supervision.
Hosted and Self-Hosted Runtimes Change the Enterprise Case
The most consequential detail in the report may be the runtime choice. TestingCatalog’s screenshots say users could select an OpenAI-hosted runtime or attach their own. The article also says Managed Agents are being designed for self-hosted environments.
An OpenAI-hosted runtime would favor convenience. OpenAI could provision the compute, maintain the execution software, stream events, and integrate tracing or evaluation data directly into the platform. Developers would avoid operating a separate agent service and could potentially move from configuration to deployment quickly.
A customer-controlled environment would favor governance and infrastructure flexibility. Enterprises may need agents to execute close to private data, use internal network services, run approved software images, or follow organization-specific security controls. A self-managed runtime could also help companies retain existing cloud commitments and monitoring systems.
“Self-hosted” should not be interpreted as OpenAI distributing its proprietary model weights. Based on the reported interface and OpenAI’s current SDK architecture, the more likely design would place the agent’s execution environment on customer infrastructure while model requests continue through an OpenAI API. The company’s revised Agents SDK already separates the agent harness from compute and permits developers to connect different compute providers.
OpenAI would not be entering an empty category. Anthropic introduced Managed Agents for enterprises in August 2026, initially through a private preview on Google Cloud’s Vertex AI. Anthropic describes its offering as a complete agent system with long-running sessions, memory, task management, permissions, integrations, and managed infrastructure. It also says customers will eventually be able to connect their own infrastructure.
Frequently Asked Questions
5 questions
1What are OpenAI Managed Agents?
OpenAI Managed Agents appears to be an unreleased platform service for creating and operating AI agents. According to TestingCatalog, developers would define an Agent, assign it an Environment, and run continuing Agent Sessions. The reported system could combine models, instructions, tools, skills, plugins, persistent state, and deployment controls in one OpenAI Platform workflow.
2
Sources
- TestingCatalog’s September 7 reporttestingcatalog.com
- https://x.com/testingcatalog/status/2096949150755471510x.com
- AgentKitopenai.com
- Agent Builder documentationplatform.openai.com
- Agents SDK runtimeopenai.com
- persistent sessionsopenai.github.io
- Developer Plugin documentationdevelopers.openai.com
