The "Performance Trap" usually happens when your backend performs eager on child records regardless of their visibility status. To fix this, I had to implement a more surgical approach to how the LLM agent handles data retrieval, ensuring that hidden records are filtered out at the database level rather than being stripped out in the application layer.
For anyone working on similar AI workflow integrations or database-heavy agents, here is the general logic for avoiding this bottleneck:
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Filter at the Source: Use indexed queries to exclude hidden records before the data hits the memory.
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Lazy : Only fetch child record details when specifically requested by the prompt engineering logic.
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Batching: Group requests for visible records to reduce the number of round-trips to the database.
It's a tedious part of the deployment process, but getting the data pipeline lean is the only way to keep the response times acceptable. If the agent is spending 2 seconds just "thinking" because it's processing 50 hidden records it can't even use, the user experience is ruined.
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