How to implement modular Extract, Cognify, Load workflows for agent memory

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Build dynamic memory for Agents and replace RAG using scalable, modular ECL (Extract, Cognify, Load) pipelines.

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1. Break down your data processing into separate extract, cognify, and load stages that can run independently and be swapped out as needed
2. Set up extraction modules that pull information from different sources like documents, APIs, or databases without hardcoding the logic into your main workflow
3. Create cognify steps that process and understand your extracted data, turning raw information into structured knowledge that agents can actually use
4. Build load components that store processed information in your agent's memory system, whether that's a vector database, graph store, or traditional database
5. Connect these modular pieces through a pipeline system that lets you mix and match components based on your specific use case and data types