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5 articles · matching “agentic ai”

  • Beyond the Hype: How Agentic AI is Redefining the Digital Economy
    Marketplace & Product Updates

    Beyond the Hype: How Agentic AI is Redefining the Digital Economy

    For the past few years, the narrative around Artificial Intelligence focused almost entirely on generative capabilities—tools that could write an essay, craft a poem, synthesize code, or design a realistic image from a single text prompt. While impressive, these systems remained fundamentally reactive: they waited for a command, produced an output, and stopped. We are entering a far more consequential shift in technology: the rise of Agentic AI.

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  • Building Agentic Workflows in Python: Beyond Basic RAG with LangGraph
    Code and Tech

    Building Agentic Workflows in Python: Beyond Basic RAG with LangGraph

    Here is a quick breakdown of what the article covers: * **The Shift:** Traditional RAG follows a rigid, linear pipeline (`Retrieve -> Generate`). **Agentic RAG** introduces dynamic, self-correcting loops using the LLM as a reasoning engine within a state machine. * **Why LangGraph:** Unlike standard DAG (Directed Acyclic Graph) pipelines, LangGraph supports **cyclic workflows** (loops), enabling self-evaluation, error handling, and tool re-invocations. * **Core Architecture:** Built around three elements: * **State:** A shared dictionary (`AgentState`) tracking history and variables across steps. * **Nodes:** Python functions that execute steps (agent reasoning, evaluation, web search). * **Edges:** Conditional routes that direct execution flow based on state evaluation. * **Production Best Practices:** Always enforce **iteration limits** to prevent infinite loops, use **lightweight models** for evaluators to reduce latency, and keep evaluation separate from tool execution.

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