Writing the loop that lets an AI agent use tools is easy. Keeping it reliable when tools fail, actions half-succeed and the model says “done” is the real work. Here is how to decide whether to own that work or buy a managed loop.
If you are choosing between OpenAI’s Agents API and connecting model calls to tools yourself, this is the question worth asking: which failures do you want your team to own? The managed option runs the conversation-and-tool loop for you. A small loop of your own gives you more direct control. Neither option takes responsibility for whether the work is actually correct.
The short answer: build when the job is narrow, repeatable and easy to check. Buy the loop when the work is open-ended enough that keeping the agent moving, remembering context and coordinating tools would become a substantial system of its own. In either case, keep your safety rules and proof of success outside the model’s final answer.
What are you buying?
An agent is not a mysterious new kind of model. It asks a model what to do next, runs a tool if needed, shows the result to the model and repeats. The model might read a file, change code, run a test and then try again.
That loop can fit in a few lines of code. The hard part sits between the arrows: What if a tool returns something unexpected? What if a write succeeds but the reply is lost? What if the model declares victory before anyone has checked the result?
With the Agents API, OpenAI runs the inner loop and maintains the session. With a DIY loop built on model calls, your application handles each turn and tool result. That distinction is real, but it is narrower than “OpenAI runs my whole agent.” In a self-hosted setup like mine, I still operated the workspace and its credentials; with either approach, I still had to define what a correct outcome meant. OpenAI’s architecture guide draws that boundary between the managed loop, your application and the execution environment.
When building a small loop makes sense
Suppose the job has three known steps: look up a record, apply a rule and write a result. You know which tools may be called and how to check the answer. A small, explicit workflow can be easier to understand than a general-purpose agent runtime. You can decide exactly when it stops, how much it may spend and which actions require approval.
It also avoids paying for more machinery than the task needs. In my one same-model, same-task comparison, both approaches fixed the bug and passed the same three tests. The DIY loop’s estimated model cost was about 2.3 US cents; the Agents API run’s was about 6.5 US cents. That is one observation, not a general pricing result. It does not include the time spent building or maintaining the DIY runtime.
Why GPT-5.4 Mini? I used the same model for both runs because the question was about the loop, not model quality. Both passed an independent check, so this particular repair did not require a frontier model. OpenAI lists Mini’s dated snapshot in its complimentary daily-token offer, which made it a practical choice for repeat trials. But “free” is conditional: the organization must qualify and opt in to sharing inputs and outputs for the project, maintain a positive balance, and stay within a shared daily cap. Tool use is excluded; overages can be billed. The costs above are list-price estimates, not a claim that either run was billed at zero.
Show the frozen settings and event evidence
This was a small, synthetic code-repair task. Both agents received the same task, starting source snapshot, tool contract and independent completion check. These are selected fields from the saved run specifications and hash-chained event logs, reformatted for readability.
Frozen settings (both runs)
Task: fix a record-normalization bug; preserve latest revision,
parse timezone-aware timestamps, and sort deterministically
Model: gpt-5.4-mini; reasoning: high; injected fault: none
Run-spec limits: 20 minutes, 60 model turns, 100 tool calls,
3 retries, US$2 maximum model spend
Completion gate: the same external three-test suite
Selected events (times relative to each run's recorded start)
Agents API · cmp2-syn-clean-a1
+01:05 tool_call command_execution started
+01:05 tool_result command_execution completed
+01:46 session_items_recovered 14 tool items
+02:11 test_result completion_gate pass
+02:36 run_completed completed
DIY loop · cmp2-syn-clean-d1
+00:48 tool_result workspace.write_file error
+00:48 error REMOTE_TOOL_ERROR
+00:52 tool_result workspace.write_file error
+00:56 tool_result workspace.execute completed
+01:06 test_result completion_gate pass
+01:07 run_completed completed
Both external test logs ended:
Ran 3 tests in 0.000s
OKThe run-spec limits are configured ceilings, not measured usage. The DIY tool errors were recovered in this run; the excerpt does not prove one loop is generally more reliable or faster. Raw tool arguments, account and environment identifiers, absolute paths and full event payloads are omitted.
Building is attractive when the workflow is small and you are willing to maintain the edges. Every odd tool response, retry after a partial write, saved-progress decision and completion check belongs to you.
When buying the loop makes sense
Now imagine a job that cannot be reduced to a short sequence: investigate an unfamiliar codebase, try several tools, revise a plan, pause and continue later. The value of a managed loop is not that it makes the model magically smarter. It is that you do not have to implement every model-to-tool-to-model exchange and maintain the session machinery yourself.
OpenAI documents session continuity, context management and other orchestration features in the Agents API. I did not test whether features such as compaction or subagents improve a long project. They are reasons to evaluate a managed service for that kind of work, not benefits my small comparison proved.
Buying the loop also does not buy the entire operation. My own evidence collector once timed out after the managed agent had done useful work. That was a failure in my application, outside the managed loop. If I choose to host the execution environment myself, I still own its setup, access and shutdown. The service changes where some responsibilities sit; it does not make them disappear.
The hidden cost is handling the messy middle
The clearest lesson from my earlier attempts was not a score for either agent. It was how easily a small piece of glue code could invalidate useful work. One unexpected tool response crashed my DIY controller before the model could react. Later, another DIY run produced a passing fix but my completion checker misread the tool result and marked it partial. The managed setup had its own failure in my evidence collection.
These are not reasons to avoid DIY—or proof that a managed service always recovers. They show what you are buying or building: the machinery that turns a messy observation into a safe next step, and the machinery that can tell you whether the job truly finished.
There is a control trade-off too. I tried to test how each agent handled a failing custom tool. The DIY agent used that tool; the managed agent took a built-in shell path and never encountered the injected failure. Both completed the coding task, but the “recovery comparison” was invalid. If a particular tool path matters for security or auditing, enforce the boundary in the environment. Do not rely on the agent choosing the route you intended.
What remains yours either way
Whatever runs the loop, you still need to answer three questions:
- Was the result correct? Run checks that do not depend on the model agreeing with itself.
- What actually changed? Record external writes and make retries safe when a response is lost.
- Can the work continue safely? Keep durable project state, permissions and a recovery path outside a chat transcript.
A model saying “done” is not a test result. A managed session is not your business record. And a cheaper model bill is not a cheaper system if the maintenance burden lands on your team.
For a concrete example of keeping the approval decision outside an agent, see Using an AI Agent for Code Review (Without Letting It Merge Code).
My decision rule
| If the work looks like this | Lean toward | Because |
|---|---|---|
| Known steps, few tools, clear stop condition | Build a small loop | You can make the workflow explicit and test every transition. |
| Open-ended, changing tools, work that may span sessions | Evaluate a managed loop | Maintaining orchestration may cost more than the code it replaces. |
| Strict control over every action and tool route | Design the execution boundary first | Neither option substitutes for permissions and independent checks. |
This was one small, matched coding task: both approaches passed. That does not tell us which option is generally faster or more reliable. The useful distinction is which parts of the agent runtime your team wants to own.
What it can tell us is where to put the decision. Do not ask whether you can write an agent loop—you can. Ask whether your team wants to run the system around it. Buy the loop if that operation is not your differentiator; build it if the work is narrow enough that explicit control is worth owning. Verify the outcome yourself either way.