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Agentic AI Engineering: A Short Practical Guide to Building Agentic Systems That Plan, Use Tools, Remember Context, and Stay Under Human Control
Paperback
Series: AI Made Practical, Book 3
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Publisher: Independently Published
Pages: 202
Weight: 0.55
Height: 0.51 Width: 6.00 Depth: 9.00
Language: English
Your AI agent worked perfectly in the demo. But will it survive the real world?
A capable language model is not enough. Once an agent begins using tools, remembering information, making decisions, and affecting real workflows, small design mistakes can turn into expensive failures.
Agentic AI Engineering is a short, practical guide for developers, AI builders, technical founders, product teams, and anyone ready to move beyond basic prompting and start designing agentic systems that can be tested, controlled, and trusted.
Instead of focusing on model training or abstract theory, this book explains the architecture surrounding the model. You will learn how to give an agent a clear objective, provide the right context, connect tools safely, manage memory, verify results, and involve human judgment before mistakes create real consequences.
Inside, you will discover how to:
Distinguish between prompts, chatbots, automations, workflows, agents, and production-ready agentic systems
Design the practical Goal, Context, Plan, Act, Check, Remember, and Escalate loop
Prevent agents from guessing when information is missing or contradictory
Build tool access that enables useful action without giving away unnecessary control
Create memory systems that preserve valuable context without becoming inaccurate or cluttered
Test realistic failure scenarios before users discover them in production
Add guardrails, approval steps, monitoring, and escalation paths that make autonomy safer
Follow a focused 30-day plan to build one useful agent around a real operational problem
Through realistic examples involving customer support, scheduling, research, invoicing, sales, and internal workflows, you will see why impressive prototypes often collapse after deployment and how stronger system design prevents those failures.
This is not a 400-page textbook filled with unnecessary complexity. It is a clear engineering framework for turning raw model capability into reliable, useful, and controlled behavior.
Because the real question is not whether your agent can act.
It is whether you can trust what it does next.
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