Managed Agent Runtime vs Building Your Own Agent
Managed agent runtimes like OpenAI's Agents API take over sessions, orchestration and recovery. Here is who owns what, when to build your own, and a five-question test.
Choosing between a managed agent runtime vs building your own comes down to one question: is the agent a means or the product? If it is a means to finish some other job, such as an internal workflow or a prototype, a managed runtime saves weeks of plumbing. If the agent is what you sell, or you need to control cost, data location and model choice, you will end up owning the loop anyway, and it is cheaper to start there.
The question became practical on 29 September, when OpenAI put its Agents API into public beta at DevDay. It is a clean example of the managed side, so this comparison uses it as the reference point and says plainly where the sources stop.
What a managed agent runtime does
According to Worthview's DevDay roundup, the Agents API is a managed cloud runtime for durable agents. Developers define the tools and environments, and OpenAI handles sessions, orchestration, context compaction and recovery. The Runtime Wire keynote roundup adds code execution and file editing, connections to MCP servers, delegation to other agents and a computer use capability for navigating a browser, and describes it as a managed Codex harness. The OpenAI developer community thread lists hosted execution, memory, tools and multi-agent coordination.
A harness is the code around a model that runs the loop: call the model, run the tools it asks for, feed results back, repeat. If that term is new, our explainer on what an agent harness is covers it. A managed runtime is a harness someone else runs. We covered the earlier launch details in our Agents API beta report.
Managed agent runtime vs building your own: who owns what
Responsibility | Managed runtime | Build your own |
|---|---|---|
Session state | Vendor keeps it | You store and load it |
Running the tool loop | Vendor orchestrates | You write and debug the loop |
Trimming long history | Vendor compacts it | You decide what to keep and summarize |
Recovery after a crash | Vendor recovers the session | You build retries and checkpoints |
Tool definitions | You define them | You define them |
Execution environment | You define it, vendor hosts it | You host and secure it |
Model choice | Usually the vendor's own models | Any model you can call |
Spending caps | Whatever the vendor exposes | Hard limits in your own loop |
Where data flows | Through the vendor's runtime | Wherever you deploy |
Switching later | Rewrite against a new interface | Mostly portable |
The top four rows are where managed earns its keep. Session storage, the tool loop, compaction and recovery are the boring parts of an agent that take the longest to get right, and they are exactly what the Agents API description says OpenAI takes over. The bottom rows are where building your own earns its keep.
When a managed runtime is the right call
Start managed when most of these are true:
The agent supports a workflow and is not your product.
You are on one model vendor already and are not planning to leave.
The work is long-running, so recovery after a failure actually matters.
You have no engineer to spare for loop maintenance.
The data involved is fine to pass through the vendor.
A solo consultant building a follow-up agent for one client fits this profile. The value is getting to a working agent this month, and every hour spent on session storage is an hour not spent on the client's actual process.
When to build your own
Build when any of these is a hard requirement:
The agent is the product. If customers pay for the agent's behaviour, you cannot hand its core loop to a vendor that can change it under you.
Multiple model vendors. A runtime tied to one vendor's models makes fallbacks and price shopping harder.
Strict cost control. In your own loop you can cut a run off at a dollar figure. Our guide to setting spending limits for AI agents shows how.
Sensitive data. If data cannot leave your environment, or you need verifiable processing, check the vendor's terms first and look at options like confidential computing for AI.
You need to see inside. Compaction is the hidden one. When a runtime summarizes old history for you, you do not control what gets dropped, and we have already seen agents hiding mistakes in handoff notes.
What is not known yet
The Agents API is in public beta, and a beta changes. None of the sources I read this run gave pricing, rate limits, a general availability date or details of how compaction works. Those are the four facts that decide whether the managed route stays cheaper than building. If you pick it, isolate the vendor-specific parts behind a thin interface of your own so a later move is a rewrite of one file, not of the whole agent.
Two projects, two answers
Take a freelancer building a weekly report agent for a single client. It pulls numbers, drafts a summary and emails it for approval. The agent is a means, the client does not care how it runs, one model vendor is fine, and the data is ordinary business metrics. The five-question test below scores it zero or one, so managed is the sensible start and the saved weeks go into the report quality.
Now take a small company whose product is an agent that books appointments for clinics. The behaviour is the product, patient data is involved, and a per-run cost cap protects thin margins. That scores four or five, and handing the core loop to a runtime whose summarizing and pricing it cannot inspect is a bet the company should not make. The same tool is right for one and wrong for the other, which is why the decision belongs to your situation and not to a benchmark.
A five-question test
Answer yes or no, and count the yeses.
Would losing this agent's exact behaviour to a vendor change hurt your business?
Do you need more than one model vendor?
Must you enforce a hard spending cap per run?
Is there data that cannot pass through a third-party runtime?
Do you need to inspect or control how history is trimmed?
Zero or one yes: go managed and revisit in six months. Two or three: prototype managed, design the interface so you can leave. Four or five: build your own loop now. For the tool landscape around all of this, see our AI coding tools guide.
FAQ
What is a managed agent runtime?
A hosted service that runs the agent loop for you. Per the DevDay coverage of OpenAI's Agents API, the vendor handles sessions, orchestration, context compaction and recovery, and you define the tools and environments.
Is it cheaper to build your own AI agent?
Not at the start. The plumbing costs engineering time before any value appears. It can become cheaper at scale or when you need hard cost controls, but no pricing for the managed option was published in the sources I read.
Can I switch from a managed runtime to my own later?
Yes, if you planned for it. Keep tool definitions, prompts and business logic in your own code and call the runtime through a thin wrapper, so only the wrapper changes.
Is the OpenAI Agents API production ready?
It is in public beta according to the DevDay announcements. Beta means the interface and terms can change, so treat it as usable for real work with a migration plan, not as a fixed foundation.
How did this land?
About the author
Staff Engineer, Platform
Carlo works on the platform that turns prompts into running apps. He writes the engineering deep dives and the changelog notes worth reading.
