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Build agents you can understand, test, and improve! This practical four-chapter ebook shows you how to choose the right level of autonomy, connect agents to useful tools, break complex processes into manageable parts, and catch weak spots with evaluation and feedback. By the end, you’ll have a solid foundation for creating more reliable agent systems and the technical judgment to take on more advanced agent development.
about the authors
Jungjun Hur is an independent AI and data engineer who specializes in building agentic systems for e-commerce. He is the author of Practical AI Application Development Using LLMs. Younghee Song is an AI consultant at PricewaterhouseCoopers (PwC). Both authors live and work in South Korea.Roberto Infante is an AI innovator with deep FinTech experience, working for a London-based hedge fund. He specializes in building agentic systems for both plain vanilla and exotic quantitative analysis.
Peter Belcak is an AI Research Scientist, currently at NVIDIA Research. Prior to that he was at Meta GenAI and ETH Zurich. He has contributed across the entire AI stack, from AI efficiency, through efficient model training and tuning frameworks to NVIDIA’s Enterprise AI agent frameworks and products. He currently researches methods for improving the reliability of agentic systems and the formation of logically correct reasoning systems, and leads product efforts on LLM hallucination elimination and LLM advertising.
Micheal Lanham is a software and technology innovator with over 20 years of industry experience. He has authored books on deep learning, including Manning’s Evolutionary Deep Learning.
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Agent Engineering ebook for free
Agent Engineering ebook for free
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Agent Engineering ebook for free
