The Innoba Books

AI Agents in Practice

Create goal-directed AI agents without losing accountability

Understand agent roles, goals, context, tools, memory, permissions, action, evaluation, orchestration, and human oversight in practical systems.

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Inside this Book

Depth you can build on

Develop a connected understanding of AI Agents in Practice, with the depth to revisit important ideas and apply them to your own work.

Pages: 347
Parts: 5
Chapters: 16

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Why this Book

Understanding that carries into practice

This Book offers a grounded alternative to describing agents as autonomous digital workers. It focuses on what an agent needs to be useful, bounded, visible, and responsible.

The professional problem

Agents can take several actions and therefore magnify both helpful work and mistakes. Without defined permissions, checkpoints, evaluation, and failure behavior, agentic systems can create operational or data risk.

What you learn

Build understanding you can use

What distinguishes an agent from a single response or fixed automation.
How to define goals, roles, inputs, outputs, tools, and limits.
How context, memory, state, and planning affect behavior.
How to orchestrate steps or specialized agents.
How to place approval, escalation, fallback, and stop conditions.
How to test success, ambiguity, and failure.
How agents fit products and workflows.

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Explore the contents

A closer look inside the Book

Follow the ideas from their foundations into practice. Each part of AI Agents in Practice develops a connected set of skills and decisions you can bring to your work.

Part 01

Understanding AI Agents

Chapter 1: From AI Assistants to AI Agents
Chapter 2: What Makes an Agent Agentic?
Chapter 3: When You Should and Should Not Use Agents

Part 02

Designing Agent Workflows

Chapter 4: Agent Goals, Roles, and Responsibilities
Chapter 5: Tasks, Steps, Plans, and Boundaries
Chapter 6: Tools, Permissions, and Action Limits

Part 03

Context, Memory, and State

Chapter 7: Context Engineering for Agents
Chapter 8: Memory, State, and Continuity
Chapter 9: Knowledge Sources and Retrieval

Part 04

Supervision, Evaluation, and Reliability

Chapter 10: Human Supervision and Checkpoints
Chapter 11: Evaluating Agent Output and Behavior
Chapter 12: Agent Failure Modes

Part 05

Practical Agent Systems

Chapter 13: Single-Agent Workflows
Chapter 14: Multi-Agent Workflows
Chapter 15: Agent Operations, Monitoring, and Improvement
Chapter 16: Your Practical AI Agent Blueprint

Core ideas

Principles that stay with you

Agency is bounded

An agent should act inside explicit permissions and scope.

Goals need criteria

Completion and quality must be defined.

Tools create consequence

System and data access require control.

Orchestration needs visibility

Multi-step work should remain inspectable.

Humans own accountability

Delegation does not transfer responsibility.

Your capabilities

Turn knowledge into professional judgment

Agent role and goal specification.
Tool and permission design.
Context, memory, and state reasoning.
Agent orchestration.
Behavior evaluation.
Review and escalation design.

Who this Book is for

Bring your context to the learning

Automation specialists, builders, consultants, operations and product professionals, and learners who already understand foundational AI and workflow concepts.

Professional applications

Put it to work

Design a reviewed research agent.
Create internal support triage.
Orchestrate specialized agents.
Build a restricted operations agent.
Choose a simpler automation when agents are unnecessary.

Connected Learning Paths

One foundation. Several ways forward

Connect this Book with complementary capabilities and work toward a specific professional outcome.

AI Automation

adds adaptable behavior.

AI Agents

forms the conceptual core.

Personal Productivity with AI

supports bounded personal delegation.

Business Automation

adds agents to operations.

Intelligent Workflow Design

supports orchestration and control.

AI Consulting & Client Solutions

supports responsible client work.

AI Implementation in Organizations

supports bounded deployment.

Career connection

Part of a larger professional capability

Agents extend the builder identity while demanding strategic judgment. You must know when agency adds value, how it fits the system, and what responsibility remains human.

Your transformation

Design agency responsibly

You should be able to specify a useful agent, define its tools and boundaries, place it inside a workflow, plan evaluation and oversight, and explain when it is appropriate.

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Our Methodology

A clear progression from learning individual skills (books) to building complex abilities (learning paths and academies) and an entire career around them.

A group of books builds learning paths; a group of learning paths builds Academies; and a group of Academies builds the AI Strategist & Builder Career.

Books
Learn a specific skill or topic
Learning Paths
Achieve a specific goal
Academies
Master a specialty
Career
Become an AI Strategist & Builder

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