Build a runnable Python harness with complete companion files, deterministic fixtures, and independent grading checks. Test controlled context variations before connecting a read-only application.
Test whether irrelevant context changes alter an AI decision. Preserve task, evidence, and policy while distinguishing genuine behavioral differences from changed settings or grading defects.
Turn context-evaluation findings into production controls. Preserve critical evidence through retrieval and summarization, assign ownership, and keep release evidence separate from runtime execution authority.
Identify what actually changes when an AI system behaves differently. Separate model parameters, instructions, retrieval, memory, tools, and feedback, then govern each layer according to its persistence and authority.
Find an architecture guide, platform, or operational problem.
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