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AI Agents for Scientists and Engineers: A Practical Guide
A chatbot can describe how to do your work. An agent can do it—if you build it to be trusted. Learn to construct, verify, and deploy AI agents that earn a place in real scientific and engineering practice.
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A practical, code-first guide to building AI agents that scientists and engineers can actually trust. Most books on large language models explain how to chat with them; this one shows how to put them to work—calling your tools, running your code, reading your data, and checking their own answers against measurable truth. Starting from first principles, you will build a complete agent loop from scratch in plain Python, then grow it into a trustworthy research instrument: giving it knowledge through retrieval with traceable citations, orchestrating many tools across multi-step pipelines, deciding when several agents help and when they merely add cost, and hardening it with guardrails, evaluation, and secure deployment. Written for researchers, engineers, and graduate students who can write intermediate Python but have never built an agent, it assumes no background in deep learning and never asks you to train a model—what you get instead is working command of one genuinely useful thing: an agent that drives your tools, does real computation on request, and tells you, honestly, when it cannot be trusted.
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Kompri Kompri
Kompri is an academic, author, investor, and educator based in Jambi, Indonesia. He holds a doctorate in Education Management and has written thirty-five books and fifty scholarly works. Alongside his academic career he builds digital tools and sells his books to readers around the world. The Expertise Engine sets down the method behind that work, so that any reader, in any country, can turn what they know into a product.
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