Choosing the right abstraction

The OpenAI Agents SDK sits on top of ideas we have already explored by building agents manually. This lesson exists to help us decide when that extra abstraction is helpful, and when it might get in the way. Making that choice well matters if we want systems that stay understandable as they grow.

Tradeoffs between manual and SDK-based agents

A manual agent gives us direct control over every part of the loop. We decide how state is stored, how decisions are made, and how tools are invoked.

The SDK introduces structure and conventions. That structure removes repetition and standardizes behavior, but it also means some decisions are made for us. Choosing between the two is always a tradeoff between visibility and convenience.

Situations where a manual agent is preferable

Manual agents work well when behavior must be tightly controlled. This is often the case for small agents, experimental logic, or systems with strict constraints.

They are also useful when we need to understand every step of execution. Writing the loop ourselves keeps the full decision process visible and easy to reason about.

Situations where the SDK adds clear value

The SDK shines when agents become larger or longer-lived. It helps manage memory, tool invocation, and workflows that would otherwise require repeated patterns of code.

When multiple tools, retries, and state transitions are involved, the SDK reduces boilerplate and encourages consistency. This makes complex agents easier to maintain over time.

Balancing control, flexibility, and simplicity

Abstraction is not all-or-nothing. We can rely on the SDK for orchestration while keeping critical logic explicit.

The goal is not to hide complexity, but to place it where it belongs. Control stays with us, while routine coordination work is handled by the framework.

Making informed architectural decisions

Choosing an abstraction level is an architectural decision, not a technical requirement. It depends on problem size, expected growth, and how much flexibility we need.

By understanding what the SDK replaces and what it leaves in our hands, we can choose deliberately instead of by habit. That choice is what keeps agent systems both powerful and understandable.

Conclusion

By this point, we are equipped to judge when the SDK helps and when it does not. We can recognize the tradeoffs, match them to our goals, and choose an abstraction level that supports the system we want to build.