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Most agent frameworks force you to learn a proprietary language just to tell the AI how to behave.

This fragmentation across prompt templates and tool schemas makes auditing an agent's logic nearly impossible.

I condensed the alternative into a visual field guide — swipe through below.

The NOOA framework changes the mechanism by treating an agent as a single Python class.

The structural logic remains deterministic, while the LLM fills in the gaps at runtime.

Here is how the mechanism works:

1. You define a Python method with a descriptive docstring and type annotations.

2. You leave the method body empty using an ellipsis (...).

3. At runtime, the LLM-driven loop reads the docstring as the prompt and the type hints as a strict contract.

4. The LLM completes the logic, but the output must match the Python type to be accepted.

For an ops leader, this turns a black box into an auditable workflow.

Instead of tracing a sprawling graph of nodes, you can pinpoint exactly which Python method was triggered during a failure.

It moves the risk from the prompt chain to a typed object that can be tested and governed like any other piece of software.

The full breakdown — including the Ellipsis Completion Cycle diagram — is in the 12-page guide below.

How would moving your agent logic into native Python classes change your current QA or auditing process?

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