The simplest AI agent code looks like this.

while not done:
    model_call()
    tool_call()
    observe()

Production quickly adds questions.

  • What if context gets too long?
  • Where do you resume if the process dies?
  • Where is mid-tool-call state?
  • What about multi-day tasks?
  • Where do sandbox files live?
  • Who manages subagents?

OpenAI's Agents API covers many of these with a managed Codex harness.

Three starting points make the choice easier

The official OpenAI guide splits new agent apps like this.

Agents API
→ OpenAI runs the managed Codex harness and durable sessions

Codex SDK
→ You run the Codex harness on your infrastructure

Responses API
→ You own model calls and the agent loop

The core difference is not the model. It is who operates the harness.

What the Agents API manages for you

Per the official docs, the Agents API manages:

  • session orchestration
  • context compaction
  • recovery
  • durable session state
  • agent loop

You can attach an OpenAI-hosted sandbox or an external sandbox when you need one.

That means you spend more time on tools and application logic.

Sometimes your own loop is better

A managed harness is not always the answer.

Your own loop may fit better when these needs are strong:

  • You control every model-call sequence
  • You run your own memory or context algorithm
  • You need a special retry policy
  • You route across multiple providers
  • You integrate tightly with an internal orchestration system
  • You optimize latency at fine granularity

You gain control. You also inherit more operations work.

CodeBridge Mini Lab: build the same tool agent twice

Assume a small repository inspector agent.

Tools:

list_files
read_file
run_tests

Version A:

hand-written while loop
hand-managed messages
hand-managed retry

Version B:

managed session
same tools
same task

Compare these:

application code lines
recovery code
state persistence
observability
average completion time
failure handling

The goal is not picking the shorter codebase. The goal is separating what you must control from what you can delegate.

Why durable sessions matter

Long agent tasks do not finish inside one HTTP request.

Task starts
→ Work progresses
→ Wait for external approval
→ Resume and continue

Agents API sessions assume this kind of durable work. Your app can send follow-up input to the same session or steer a running agent.

That persistence changes how you design approvals and long tasks. You no longer rebuild context from scratch each turn.

Sandboxes and harnesses are different layers

This distinction matters too.

Harness
→ orchestration: what to do next

Sandbox
→ execution environment: files, commands, packages

You can use a managed harness with your own sandbox. Or you can operate both yourself.

Choose each layer on its own merits. Do not bundle them by accident.

Conclusion: the Agents API choice is about ownership, not convenience

Agent frameworks look similar in feature lists.

Ask the better question instead.

Who owns context, retry, recovery, state, and sandbox?

If orchestration itself is your product edge, owning it pays off. If orchestration is shared infrastructure, a managed harness often wins on speed and operational stability.

Further reading

References

Go deeper with a course

If you want to practice building control, memory, and verification gates into a real project, this course follows the ownership trade-offs in this post.