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Autor(en): 
  • Jeff Jones
  • Building Agents That Work: A Step-by-Step Guide to Agent Design - M365 Copilot Edition 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Vorankündigung
    Veröffentlichung:  ANGEKÜNDIGT (Juni 2026)  
    Genre:  EDV / Informatik 
     
    COMPUTERS / Artificial Intelligence / Generative AI / COMPUTERS / Enterprise Applications / General / COMPUTERS / Intelligence (AI) & Semantics
    ISBN:  9781966835042 
    EAN-Code: 
    9781966835042 
    Verlag:  Iter8n 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 254 mm / B 178 mm / D 14 mm 
    Gewicht:  465 gr 
    Seiten:  264 
    Bewertung: Keine Bewertung vor Veröffentlichung möglich.
    Inhalt:

    Building Agents That Work is for information workers, managers, analysts, and team leads who want to build useful agents in Microsoft 365 Copilot without turning the whole thing into a software project. It is written for people who need agents to do real work more than once, not just survive one impressive test run.

    This edition uses M365 Copilot and Agent Builder, and it teaches the subject by having the reader build as they go. It starts with the problem many people hit early: a role prompt or rough instruction block looks fine at first, then starts slipping. The answer is not endless prompt tweaking. The book shows how to give the agent a stronger structure, separate standing rules from task-specific guidance, organize supporting files, route work more deliberately, and test results so the system holds up better over time.

    The material builds in sequence. Readers start with how an agent's instructions actually work, then move through starter patterns, a worked example, and the shift from a single-file build to a modular file set. You build and test as you go, which means the ideas do not stay abstract for long.

    There is also a companion repository with starter and example files. That matters because the book is meant to leave the reader with something they can use, not just something they understand. By the end, readers come away with a mental model for how dependable agents are put together, along with starter materials they can adapt to their own work in communication, analysis, planning, operations, and other knowledge-work settings.

    This is not a coding manual, a hype book, or a bag of prompt tricks. It is a practical learning guide for people who want to build, test, and refine Copilot agents, then leave with a framework and a set of starting files they can actually use.

      



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