Contents

Why this book and the computational viewpoint it develops.

Suggested paths for students, engineers, and researchers.

The progression from foundations to working systems.

The central problem, missing abstraction, and core proposition.

Identity, scope, provenance, activation, contribution, and conflict.

How problem formulation changes when knowledge becomes first-class.

Knowledge acquisition, representation, activation, and lifecycle.

A reusable abstraction for packaging knowledge and computational behavior.

How multiple forms of knowledge participate in a conclusion.

Building a small system without hiding the knowledge in prompts.

Integrating modern AI components without surrendering the architecture.

Evaluation, provenance, failure analysis, and software quality.

A complete reference application built around active knowledge.

Knowledge, constraints, evidence, and consequence-aware decisions.

Planning, action, memory, and knowledge-aware coordination.

Compositional operations over knowledge and their semantics.

What remains unresolved and where the field may go next.

Code organization, examples, testing, and contribution workflow.

Core terms and working definitions.