The Enterprise AI Agents Operating Manual
A sober production manual for designing, permissioning, testing, monitoring, and governing AI agents before they touch live workflows, customer data, or operational systems.
EUR 197
Price
Online
Format
260 pages
Pages
Agent operating model
Human approval architecture
Monitoring and incident framework
Who It Is For
Built for people making business decisions.
CIOs, CTOs, AI leaders, security teams, operations executives, and implementation teams deploying agentic AI beyond controlled demos
Use the book as an operating manual for planning AI automation, selecting the right workflows, governing risk, measuring ROI, and moving from isolated pilots to a managed automation portfolio.
Business Outcomes
Move agents beyond demos without losing control
Define tool permissions, identity, escalation, and audit trails
Operate agent systems with testing, monitoring, and incident response
Reader Signal
Built for operators who need more than AI hype.
The book is positioned for executives and implementation teams who need a practical path from pilot activity to managed automation capability.
"
The useful part is the operating model. It does not sell AI as a tool purchase; it shows how to make ownership, governance, and ROI clear enough for leadership to act.
COO, mid-market services firm
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The value map and stage-gate model make the book practical. It gives teams a way to decide what should be automated first and what should be killed before it wastes budget.
Transformation lead, B2B operations
"
This reads like a field manual for executives. The strongest sections are governance, operations, and measurement because they address what usually breaks after the demo.
CIO advisor, enterprise AI programs
Inside the Website Reader
Chapter preview.
Why Agents Are Operationally Different
Explains when autonomous or semi-autonomous AI agents are useful, and where tool access, permissions, and approvals must be controlled.
The Enterprise Agent Operating Model
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
Choosing Workflows for Agentic Systems
Explains how to move from a business process to a working AI-enabled system with clear inputs, owners, and outputs.
Tool Use, Permissions, and Identity
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
Memory, Context, and Retrieval
Shows how business knowledge, retrieval, source quality, and context design determine whether AI output can be trusted.
Human Approval and Escalation Design
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
Evaluation, Simulation, and Red Teaming
Builds the testing discipline needed to catch weak outputs before users or customers depend on them.
Monitoring, Drift, and Incident Response
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
Agent Security and Data Boundaries
Maps the data, access, vendor, and compliance questions that must be answered before scaling.
Governance, Auditability, and Ownership
Defines the controls, review points, ownership, and evidence needed to use AI without creating unmanaged exposure.
The Enterprise Agent Rollout
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
From Single Agent to Agent Operations
Shows how to turn the chapter topic into a clear business decision, operating model, or implementation step.
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Stay inside the Neural Mode research system.
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