Imran Ahmad takes a project-based approach to teaching agentic AI engineering, walking through 30 distinct agent builds grounded in proven architectures and patterns. At 542 pages, this is a substantial, practical resource for engineers who learn best by building real systems rather than reading theory.
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In StockRather than explaining agent concepts in the abstract, 30 Agents Every AI Engineer Must Build takes the more direct route of just building them — thirty times over, across a range of architectures and use cases. Imran Ahmad structures the book around proven patterns rather than experimental approaches, giving engineers a library of tested designs they can adapt to their own production needs instead of reinventing solutions from scratch. The sheer breadth here, spread across 542 pages, means the book functions almost like a reference catalog: readers can move directly to the agent type relevant to their current problem rather than working through the entire book linearly. Each build emphasizes production readiness over toy examples, addressing the kinds of edge cases and reliability concerns that matter once an agent moves beyond a demo environment. For engineers who want to build real competency through repetition and varied, hands-on examples rather than conceptual explanation alone, this offers an unusually dense and practical resource.
Author: Imran Ahmad.
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