Rewriting a manual agent using the SDK

Up to this point, we have built agents by hand. We wrote our own loops, managed state ourselves, and wired together reasoning, tools, and memory explicitly. That work was necessary to understand what an agent really is.

This lesson exists to show how that same agent can be expressed using the OpenAI Agents SDK, without changing what the agent does. The focus is not on new capabilities, but on migration: taking something that already works and rewriting it in a more structured, SDK-managed form.

Identifying the components of a manually implemented agent

A manual agent usually breaks down into a small set of recognizable pieces. There is state that represents what the agent knows. There is a loop that runs repeatedly. There is decision logic that chooses what to do next. And there are tools that perform concrete actions.

Even when these pieces are spread across files, they are conceptually distinct. Being able to point to them is the first step in rewriting the agent using the SDK.

Mapping manual components to SDK constructs

The SDK provides named constructs that correspond closely to those manual pieces. Agent state maps to SDK-managed context and memory. Decision logic maps to model-driven reasoning configured on the agent. Tools map to registered Python functions.

The key shift is that control flow moves out of our code and into the SDK. Instead of calling the model, selecting tools, and updating state ourselves, we describe those parts and let the SDK coordinate them.

Replacing custom control loops with SDK-managed execution

In a manual agent, the main loop is explicit. We read input, decide, act, and update state in a while loop. With the SDK, that loop is implicit.

We invoke the agent, provide input, and allow the SDK to manage the reasoning and action cycle. Our code no longer advances the loop step by step. It hands off control and reacts to results instead.

Preserving agent behavior during the rewrite

Rewriting an agent is not about changing its personality or capabilities. The goal is behavioral equivalence. The same inputs should lead to the same kinds of actions and outputs.

This usually means reusing existing tools unchanged and carrying over the same instructions and constraints that guided the manual agent’s decisions. The SDK replaces scaffolding, not intent.

Verifying functional equivalence after migration

Once the rewrite is complete, the agent should be exercised in familiar scenarios. We look for the same decisions, the same tool calls, and the same observable outcomes.

If the SDK-based agent behaves differently, the cause is usually configuration, not logic. Verifying equivalence confirms that the SDK has replaced infrastructure, not understanding.

Conclusion

By identifying the pieces of a manual agent and mapping them directly onto SDK constructs, we can rewrite an existing agent without changing what it does. The SDK takes over control flow and coordination, while we retain responsibility for behavior and intent.

At this point, we are no longer building agents from scratch. We are choosing a runtime that lets us express them more clearly and with less boilerplate.