Gyan Prakash, Saumya Singh

Engineering Intelligence going one Loop at a Time! When we first began exploring Agentic AI, we approached it from different professional paths — yet arrived at the same underlying tension. On one side, the technology appeared powerful and elegant in theory, but frustratingly opaque in practice. Large language models and autonomous agents demonstrated remarkable capabilities, yet their behaviour often felt difficult to reason on, difficult to validate, and even harder to operate with confidence in real-world systems. They were deemed for being supportive than distinguished deciders. I realisRead More...

Engineering Intelligence going one Loop at a Time!

When we first began exploring Agentic AI, we approached it from different professional paths — yet arrived at the same underlying tension. On one side, the technology appeared powerful and elegant in theory, but frustratingly opaque in practice. Large language models and autonomous agents demonstrated remarkable capabilities, yet their behaviour often felt difficult to reason on, difficult to validate, and even harder to operate with confidence in real-world systems. They were deemed for being supportive than distinguished deciders.

I realised it early on from years spent working in technology, agentic systems felt both familiar and incomplete. They 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 evaluated context, selected actions, observed outcomes, and adjusted behaviour — not unlike the control loops already embedded in modern software and infrastructure. At the same time, partner in crime Saumyah, with her technological experience highlighted equally important set of 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?

These are questions that have haunted the engineering corridors for years. And are more prevailed upon now that there is a huge customer base using AI for their daily life.

Additionally, how do we reason about edge cases when system behaviour is no longer strictly deterministic?

These questions were not philosophical curiosities. They were practical engineering concerns — the same concerns that surface whenever systems move from controlled experimentation into production environments where reliability, safety, and accountability matter.

If you have had questions like this too then, congratulations! Your ultimate reference book is here.

All answers in one book.

The Algorithmic Loop was born at the intersection of above perspectives. This book treats Agentic AI not as an abstraction, but as engineering. As a one stop solution. In this book we will frame agentic systems as loops — algorithmic at their core — where inputs, state, decisions, actions, and outcomes can be observed, measured, tested, and improved. While the models inside these systems may be probabilistic, the systems themselves do not have to be mysterious. Our goal is clarity. Our aim is demystification. By distilling modern agentic architectures into repeatable patterns, we aim to make autonomy understandable to architects, developers, testers, and technical leaders alike — not as a black box, but as a set of deliberate design choices. The book is written to be approachable for readers new to agentic concepts, while still offering the depth, rigor, and architectural insight required by experienced practitioners.

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The Algorithmic Loop

Books by Gyan Prakash, Saumya Singh

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 patt

The Algorithmic Loop

Books by Gyan Prakash, Saumya Singh

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.

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Engineering Intelligence going one Loop at a Time!
When we first began exploring Agentic AI, we approached it from different professional paths — yet arrived at the same underlying tension. On one side, the technology appeared powerful and elegant in theory, but frustratingly opaque in practice. Large language models and autonomous agents demonstrated remarkable capabilities, yet their behaviour often felt difficult to reason on, difficult to validate, and even harder to operate with confidence in real-world systems. They were deemed for being supportive than distinguished deciders.
I realised it early on from years spent working in technology, agentic systems felt both familiar and incomplete. They 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 evaluated context, selected actions, observed outcomes, and adjusted behaviour — not unlike the control loops already embedded in modern software and infrastructure. At the same time, partner in crime Saumyah, with her technological experience highlighted equally important set of 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? 
These are questions that have haunted the engineering corridors for years. And are more prevailed upon now that there is a huge customer base using AI for their daily life. 
Additionally, how do we reason about edge cases when system behaviour is no longer strictly deterministic?
These questions were not philosophical curiosities. They were practical engineering concerns — the same concerns that surface whenever systems move from controlled experimentation into production environments where reliability, safety, and accountability matter. 
If you have had questions like this too then, congratulations! Your ultimate reference book is here.
All answers in one book. 
The Algorithmic Loop was born at the intersection of above perspectives. This book treats Agentic AI not as an abstraction, but as engineering. As a one stop solution. In this book we will frame agentic systems as loops — algorithmic at their core — where inputs, state, decisions, actions, and outcomes can be observed, measured, tested, and improved. While the models inside these systems may be probabilistic, the systems themselves do not have to be mysterious. Our goal is clarity. Our aim is demystification. By distilling modern agentic architectures into repeatable patterns, we aim to make autonomy understandable to architects, developers, testers, and technical leaders alike — not as a black box, but as a set of deliberate design choices. The book is written to be approachable for readers new to agentic concepts, while still offering the depth, rigor, and architectural insight required by experienced practitioners.

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Gyan Prakash, Saumya Singh

The Algorithmic Loop

Books by Gyan Prakash, Saumya Singh

The Algorithmic Loop

Books by Gyan Prakash, Saumya Singh

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