Knowledge base rotation - The pattern of overwriting your knowledge base with the latest data after each run. Today's "new" scrape becomes the "old" baseline for future comparisons. This is what gives the agent memory across runs without requiring a database.
Structured outputs for decision-making - Instead of having the LLM return freeform text, you define specific output variables (a boolean, an analysis string, a severity level) that downstream nodes can act on programmatically. This is what makes conditionals possible after an LLM step.
result_1 through result_5A10:E10 from the Google Sheet. Output: knowledge_base_statusdata (returns true or false)dataIf false (no existing data):
A10 through E10)If true (data exists):
A10:E10. Output: old_datatrue/false)high OR mediumtest.com (or any throwaway URL) when testing your API call - you just need to confirm the scrape works, not analyze real data yetcheck_knowledge_base, old_data, slack_notification - not defaults
Knowledge base rotation - The pattern of overwriting your knowledge base with the latest data after each run. Today's "new" scrape becomes the "old" baseline for future comparisons. This is what gives the agent memory across runs without requiring a database.
Structured outputs for decision-making - Instead of having the LLM return freeform text, you define specific output variables (a boolean, an analysis string, a severity level) that downstream nodes can act on programmatically. This is what makes conditionals possible after an LLM step.
result_1 through result_5A10:E10 from the Google Sheet. Output: knowledge_base_statusdata (returns true or false)dataIf false (no existing data):
A10 through E10)If true (data exists):
A10:E10. Output: old_datatrue/false)high OR mediumtest.com (or any throwaway URL) when testing your API call - you just need to confirm the scrape works, not analyze real data yetcheck_knowledge_base, old_data, slack_notification - not defaults