Multi-Agent Intelligence for Next-Gen Insights
summary
Claims end-to-end enterprise analysis drops from days to ~20 minutes by orchestrating specialized agents—research, analysis, domain expert, quality—that extract CRM, app reviews, tickets and social feeds, identify a ~20% satisfaction drop in late November, correlate it to a mobile update and UI navigation changes, and propose remediation with projected recovery timelines. Implementation uses JavaScript event‑driven architecture, NodeJS microservices, LangChainJS chains/agents, vector databases for RAG, message brokers and Express/Nest for orchestration. Works with caveats: latency, batching, distributed logging and bias detection remain engineering challenges.
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chapters (12)
- 0:00Introduction and Overview
- 1:16Understanding the Search Problem
- 2:58Evolution of Search Paradigms
- 3:56Introduction to Agent Search
- 4:36Practical Examples of Agent Search
- 7:12Multi-Agent Architecture Explained
- 9:12Why JavaScript for Multi-Agent Systems
- 10:08Core Building Blocks of Agent Search
- 11:16Implementation Pathway
- 13:26Real-World Impact and Benefits
- 14:32Challenges and Future Considerations
- 16:06Conclusion
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