Yes, and I am especially grateful that you did not give me one rigid implementation.
At first, your answer seemed broader than the literal question I had asked. But that turned out to be valuable. If you had given me one concrete architecture, I probably would have attached myself to it and tried to reproduce it exactly. Instead, I had to think through several possible ways for Python and /goal to cooperate.
What I originally imagined was a more direct interface: /goal would pass work to Python, Python would process it, and /goal would receive the result and decide what to do next.
In practice, I did not begin by designing that complete interface. I first needed to prove that deterministic scripts could actually replace the expensive repetitive processing that had consumed my limit without completing the task.
So I worked slice by slice:
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I divided the larger task into separate processing stages.
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I created a Python script for each stage.
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I ran each script independently and inspected its output.
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Once several stages were reliable, I connected them into one pipeline.
The practical interface became much simpler than I had initially imagined. Each stage receives defined inputs and configuration, performs deterministic processing locally, and produces structured outputs such as CSV files, status information, and logs.
The model does not need to reason over every record. Its role is mainly to help design or modify a stage, run the workflow, inspect unexpected results, distinguish data problems from implementation defects, and adapt the code when the assumptions change.
After the first three stages worked, I started building a dashboard so that I could configure and run the pipeline more conveniently. That exposed an architectural weakness: my original scripts were too narrowly coupled to one specific use case. I then had to separate the reusable processing logic from the adapters, configuration, and interface layer.
My current process is therefore:
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define and test one narrow slice;
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automate it with a deterministic script;
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validate the output;
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generalize its interface where necessary;
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add it as the next stage of the pipeline and expose it through the dashboard.
The most important thing I learned is that the interface between Python and /goal is not primarily about a sophisticated integration mechanism. It is about having clear contracts between stages: explicit inputs, deterministic processing, structured outputs, and visible failures.
The model is most valuable outside the repetitive processing loop—where interpretation, debugging, architectural changes, and handling exceptions are required.
So your answer was not evasive at all. By leaving the implementation open, you gave me room to discover an architecture that matched the actual problem instead of forcing the problem into a predefined solution. Thank you again for that.