Building Agentic AI Workflows

The last decade of AI has been shaped primarily by passive generative models-systems that excel at producing outputs when asked, but that do not initiate, plan, or act in the world. As organizations increasingly need intelligent systems capable of autonomy, adaptation, and goal-driven behavior, a new paradigm has emerged: Agentic AI.

Agentic AI elevates models from static tools to active, decision-making entities that perceive, reason, act, and learn continuously. Instead of generating content on demand, Agentic AI systems operate as autonomous software components-capable of coordinating workflows, manipulating tools, adapting to environments, and collaborating with other agents or humans.

Building Agentic AI Workflows attempts to document the full landscape of modern agentic systems - from cognitive foundations and memory architectures to multi-agent orchestration, tool-use pipelines, reliability engineering, and enterprise-scale deployment.

It has a simple goal: provide a clear, engineering-ready guide to designing, deploying, and scaling agentic systems - complete with flow diagrams, real-world examples, and hands-on code. It provides the necessary tools to build the next generation of adaptive AI systems.

It is divided in 2 parts. Part-I focuses on the cognitive, mathematical, and architectural principles that define what an AI agent is and how it behaves within an environment. It dives deep into topics like Intro to Agentic AI & its Computational Foundations, Modular Agent Design, Building the Agentic AI Stack, Python Engineering Toolkit, and Agent Memory & Learning. Part-II expands from theory into real-world engineering, where agents operate as part of larger, structured workflows. It dives deep into topics like Multi-agent collaboration & communication protocols; Workflow orchestration & role delegation; Action pipelines, Agentic evaluation, metrics, and safety constraints; Infrastructure, APIs, and deployment at enterprise scale; and production-ready architectures.