In an era where AI is no longer just a tool, but a partner in creation and decision-making, understanding the architecture of intelligent systems is paramount.
The Algorithmic Loop introduces a clear, structured framework for building and reasoning about agentic systems. It explores how perception, reasoning, action, and feedback continuously interact to shape intelligent behavior. By grounding abstract AI concepts in practical design patterns, the book helps readers move from theory to real-world implementation.
Whether you are designing autonomous agents, intelligent workflows, or adaptive systems, this book provides a foundational lens to think clearly about how modern AI systems operate — and how they should be built.
What you’ll discover inside:
The Foundations of Agentic AI
Modern Design Patterns for Intelligent Systems
Case Studies & Practical Applications
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Gyan Prakash, Saumya Singh
When we first began exploring Agentic AI, we came from different professional paths yet encountered the same tension. The technology was powerful and elegant in theory, but frustratingly opaque in practice. Large language models and autonomous agents showed remarkable capability, yet their behaviour was difficult to reason about, validate, and operate confidently in real-world systems. They were seen as supportive tools rather than distinguished decision-makers.
From years in technology, I recognised something both familiar and incomplete. Agentic systems resembled distributed systems with feedback loops — pipelines infused with intent and infrastructure that no longer merely executed instructions but actively participated in decision-making. These systems evaluate context, select actions, observe outcomes, and adjust behaviour — much like the control loops embedded in modern software and infrastructure.
At the same time, my partner-in-crime Saumyah, drawing from her technological experience, raised equally critical questions:
How do we test a system that reasons?
How do we validate autonomy under stress, uncertainty, and partial failure?
What does regression mean when behaviour evolves over time?
How do we reason about edge cases when behaviour is no longer strictly deterministic?
These are not philosophical curiosities. They are practical engineering concerns — the same ones that surface when systems move from controlled experimentation into production environments where reliability, safety, and accountability matter. They have long echoed through engineering corridors and are even more pressing today, with a vast customer base relying on AI in daily life.
If you have wrestled with these questions too, congratulations — your ultimate reference book is here.
All the answers. One book.
