Identifying what the SDK replaces

This lesson exists to make the transition to the OpenAI Agents SDK feel concrete. We have already built agents by hand, so we know how much supporting code is required just to keep an agent running safely. The SDK steps in to absorb a large portion of that work, letting us focus on behavior instead of scaffolding.

Our goal here is to recognize which parts of our existing code disappear, which parts move into the SDK, and which parts still belong firmly in our own programs.

Boilerplate eliminated by the SDK

Manual agents tend to accumulate a lot of structural code before they do anything useful. We write loops, dispatch logic, context handling, and glue code just to keep the agent alive.

With the SDK, that boilerplate vanishes. We no longer write our own main loop, prompt assembly logic, or response handling pipelines. The SDK provides a ready-made runtime that repeatedly reasons, selects tools, and updates state without us wiring those steps together explicitly.

The result is less code whose sole purpose is “keeping the agent running.”

Responsibilities shifted from user code to the SDK

Some responsibilities do not disappear, but they move out of our hands. Conversation state, tool exposure to the model, and turn-by-turn execution are now owned by the SDK.

Previously, we passed state into the model, received output, and manually decided what to do next. With the SDK, those steps are coordinated for us. We still define instructions, tools, and memory settings, but we no longer orchestrate each interaction ourselves.

This shift reduces cognitive load and makes agent behavior easier to reason about at a high level.

Code that must still be written explicitly

The SDK does not remove the need for real logic. We still write tools as Python functions. We still decide what state matters, what actions are allowed, and what success looks like.

Domain-specific behavior remains our responsibility. If an agent generates an HTML page, queries a dataset, or validates input, that code is still ours. The SDK provides the runtime, not the substance.

In practice, the code that remains is the code that actually expresses intent.

def render_planet_page(planet):
    return f"<h1>{planet['name']}</h1>"

Changes in error-handling and state management

Manual agents often include defensive code around every step: checking outputs, catching failures, and restoring state. The SDK centralizes much of this behavior.

Errors are surfaced through SDK-managed mechanisms rather than scattered try blocks. State is tracked consistently across turns instead of being rebuilt each iteration. We still respond to failures, but we do so from clearer signals.

This changes error handling from a low-level concern to a design decision.

Simplifications introduced by SDK abstractions

The most important simplification is conceptual. Instead of thinking in terms of loops, prompts, and dispatch tables, we think in terms of agents, tools, memory, and tasks.

The SDK’s abstractions collapse many moving parts into named concepts with clear boundaries. That makes agent code shorter, easier to scan, and easier to modify without unintended side effects.

The payoff is not just fewer lines of code, but fewer things to hold in our heads at once.

Conclusion

At this point, we can clearly see what the SDK replaces and why it matters. Boilerplate shrinks, responsibilities shift upward, and our code becomes more focused on behavior rather than machinery.

We are now oriented to what disappears, what remains, and what improves when moving from a DIY agent to an SDK-managed one.