Mapping the SDK to the agent loop

Up to this point, we have built agents by hand, wiring together sensing, decision-making, action, and state updates ourselves. That work makes the agent loop concrete, but it also reveals how much repetitive structure is involved. This lesson exists to show how the OpenAI Agents SDK lines up with that familiar loop and how it takes over some of the mechanical work while preserving the same underlying model.

Mapping SDK concepts to the Sense–Decide–Act–Update cycle

The SDK does not replace the agent loop; it formalizes it. The same four phases still exist, but they are expressed through SDK concepts rather than explicit loops and function calls.

Sensing typically corresponds to inputs passed into an agent invocation. Deciding is handled by the model-driven reasoning step managed by the SDK. Acting occurs when the SDK selects and runs a tool. Updating happens as the SDK records results into conversation state or memory.

Conceptually, we can still sketch the loop in the same way as before, even though we no longer write it explicitly.

while True:
    sense()
    decide()
    act()
    update()

The difference is that the SDK owns the loop mechanics, not the mental model.

Where the SDK fits into agent control flow

In a manual agent, we controlled the entire flow from start to finish. With the SDK, control flow is shared. We initiate an agent run, and the SDK takes responsibility for progressing through reasoning and tool use until a response is produced.

Our code still decides when to invoke the agent and what input to supply. Once invoked, the SDK drives the internal steps that would otherwise live inside our own loop.

This places the SDK squarely in the middle of the agent’s control flow, acting as the runtime that coordinates reasoning and action on our behalf.

Responsibilities delegated to the SDK

Several responsibilities that we previously implemented manually are delegated to the SDK. These include maintaining conversational context, coordinating model calls, selecting tools, and routing tool results back into the reasoning process.

We no longer need to write glue code to pass state into the model or to interpret raw model output for tool selection. The SDK standardizes these steps and executes them consistently.

This delegation reduces boilerplate, but it also means some behavior is now implicit rather than spelled out line by line in our own code.

Relating SDK constructs to manual components

Most SDK constructs have direct analogues in the agents we built earlier. An SDK-defined agent corresponds to our hand-written agent loop. Tools correspond to callable capabilities we previously stored in dictionaries or tables. Memory maps to the state structures we persisted manually.

Seeing these relationships makes the SDK easier to reason about. We are not learning a new kind of agent; we are using a structured wrapper around the same components.

The mental shift is from assembling the loop ourselves to configuring a loop that already exists.

The SDK as a structured agent runtime

Taken together, the SDK acts as a structured runtime for agents. It enforces a consistent flow, manages context, and coordinates actions, while still relying on us to define goals, tools, and boundaries.

Thinking of the SDK this way helps set expectations. It is not magic, and it is not a black box replacement for understanding agents. It is an execution environment that embodies the same loop we already know, but in a reusable and disciplined form.

Conclusion

By mapping the OpenAI Agents SDK onto the familiar sense–decide–act–update cycle, we can see that nothing fundamental has changed. The agent loop is still there, just expressed through higher-level constructs. At this point, we are oriented to how the SDK fits into agent control flow and how it relates to the manual agents we have already built.