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The agent performs well in simulation.When deployed in the real world, it makes strange decisions.The physics is slightly different.Small changes lead to big failures.
Data was clean initially, but inconsistencies have crept in. Fields mean different things across records. Reports are harder to trust. I want to understand why this happens.
BIASed model issue
loop redirectiing fix
Business rules often evolve gradually rather than changing all at once.Testing new logic without disrupting live processes is critical.Traditional Apex-based approaches make this risky.
I trained a model that performed really well during experimentation and validation.The metrics looked solid, and nothing seemed off in the notebook.However, once deployed, predictions started becoming unreliable within days.I’m struggling to understand why production behavior is ...
