InBeyond The AlgorithmbyRaviNuvetric·Sep 18How Would You Make a LangGraph Agent Fault Tolerant?A practical guide to checkpoints, retries, recovery, validation, human intervention, and safe execution for production AI agents
RaviNuvetric·Sep 16dbt and Data Modeling: Building Reliable Analytics for the AI EraA practical guide for Senior Data Analysts and Data Engineers on transforming raw warehouse data into trusted, reusable, and AI-ready…
RaviNuvetric·Sep 16Cloud Data Warehousing in the AI Era: From Reliable Analytics to AI-Ready Data PlatformsA practical guide to architecture, modeling, performance, governance, and AI-ready data systems from a Senior Data Analyst and Data…
RaviNuvetric·Sep 7Memory, Checkpoints, and Resume Logic: How to Build Long-Running AI Agents That Don’t Forget or…A practical guide to memory, checkpoint persistence, and resuming long-running agent workflows
RaviNuvetric·Sep 6How to Build AI Agents That Can Take Actions Safely?A practical guide to human-in-the-loop approval, validation gates, restricted tools, checkpoints, and audit-ready execution workflows.
RaviNuvetric·Sep 6How Would You Make an AI Analytics Agent Reliable?Building validation gates, evidence checks, and safe fallback paths for production-grade AI analytics workflows
RaviNuvetric·Sep 4Building a Multi-Step KPI Investigation AgentHow Agentic AI can help product teams diagnose activation drops, identify root drivers, and generate structured business recommendations
InBeyond The AlgorithmbyRaviNuvetric·Sep 3Query Routing and Tool Selection in Agentic AI SystemsThis is where Query Routing and Tool Selection become important. In a simple chatbot, every question may be sent directly to the LLM.
RaviNuvetric·Sep 2Evaluating RAG Systems the Right Way: A Practical Framework for Retrieval and Generation QualityRetrieval-Augmented Generation, or RAG, has become one of the most practical ways to make Large Language Models useful for real business…A response icon1A response icon1
RaviNuvetric·Sep 2Why RAG Systems Fail: A Practical Guide to Debugging Retrieval, Ranking, Chunking, Metadata, and…RAG, or Retrieval-Augmented Generation, is one of the most practical architectures for building AI applications on top of private or…
