Agentic AI

AI agents are moving from demonstrations to real work. They can research a question, inspect records, call approved tools, draft an artifact, and decide what to do next. That ability creates opportunity, but it also changes the engineering problem. A fluent model response is no longer enough when software can send a message, update a record, open a ticket, or trigger a workflow.
Agentic AI gives you a practical framework for building autonomous systems that are useful, observable, and accountable. It starts with the fundamentals: what makes a system agentic, how an agentic control loop works, and why models, tools, state, and permissions must be designed together. It then moves into the work that determines whether an agent can be trusted in production: task contracts, reliable tool use, grounding, memory, multi agent systems, human review, evaluations, traces, incident response, cost control, and capacity planning.
You will also explore agentic applications in software engineering, research, customer operations, and internal workflows. Later chapters address the hard questions that cannot be postponed: prompt injection, data governance, privacy, safety, compliance, accountability, and organizational adoption.
This book does not ask you to hand your business to an autonomous system. It shows you how to allocate autonomy carefully. You will learn to define a narrow objective, give an agent the least authority it needs, validate its work with realistic cases, and keep people responsible for consequential decisions.
Whether you are planning your first agent or reviewing a portfolio of AI initiatives, Agentic AI will help you separate useful automation from vague promises and build systems that can earn more responsibility over time.