How Much Should You Trust an AI Agent? : A Research-Based Framework for Delegation and Job Design
Language: English
Published by Independently Published Aug 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 47.04
Quantity: 2 available
Add to basketItem description from seller
Neuware - As AI agents absorb more of the execution work inside organizations, the scarce resource shifts from labor to calibrated judgment about how much autonomy to grant. Most organizations get that calibration wrong in one of two predictable directions - over-supervising until the review queue erases the productivity gain, or under-supervising until nobody can say who was accountable when it mattered.How Much Should You Trust an AI Agent is a research-first business book that tests the confident claims circulating about agentic AI - on delegation, oversight, job design, and job titles - against the automation, principal-agent, and organizational-design research those claims usually skip. Three separate times, a claim of 'unprecedented novelty' fails to survive contact with the actual evidence, while a narrower, more useful finding holds up again and again: oversight and accountability work when their form is real, not merely when they're present.Built from decades of automation and human-factors research, principal-agent economics, job-design theory, and 2025-2026 findings on AI-agent oversight - checked against primary sources throughout, with what remains genuinely uncertain disclosed rather than smoothed over - the book culminates in the Load-Bearing Framework: a practical, evidence-grounded tool for deciding how much autonomy a specific AI agent needs, and what real accountability requires once it has it. A dedicated chapter scales the framework for organizations without a dedicated AI or platform team - the majority of real-world readers making this decision today.What you'll find inside: - Why the 'AI delegation is unprecedented' claim doesn't survive scrutiny - and what actually is different (Chapter 1)- Why 'more human oversight is always safer' is false, and what the evidence says instead (Chapter 2)- An honest staging of the industry's live debate over whether AI oversight is permanent or transitional (Chapter 3)- What job-design research says about redesigning work around AI agents (Chapter 4)- How many AI agents one person can actually oversee - and why 'as many as you want' is wrong (Chapter 5)- The Load-Bearing Framework: a complete, worked decision tool (Chapter 6)- Whether 'AgentOps Engineer' and similar titles are real new careers (Chapter 7)- A full, honest catalog of what the research doesn't yet answer (Chapter 8)- A dedicated version of the framework for organizations without a dedicated AI team (Chapter 9)- A practical First-90-Days roadmap for applying all of itWritten for the business leader, operations executive, or department head deciding - this quarter - how much autonomy to grant an AI agent inside a real process. Not a technical book for AI engineers; a design book for the people responsible for the organization the agent works inside.…
Seller Inventory # 9798192004708
- Title
- How Much Should You Trust an AI Agent? : A Research-Based Framework for Delegation and Job Design
- Author
- Payam Haerifar
- Publisher
- Independently Published Aug 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798192004708
- Item weight
- 292 grams
- Dimensions
- 229x152x12 mm
As AI agents absorb more of the execution work inside organizations, the scarce resource shifts from labor to calibrated judgment about how much autonomy to grant. Most organizations get that calibration wrong in one of two predictable directions — over-supervising until the review queue erases the productivity gain, or under-supervising until nobody can say who was accountable when it mattered.
How Much Should You Trust an AI Agent? is a research-first business book that tests the confident claims circulating about agentic AI — on delegation, oversight, job design, and job titles — against the automation, principal-agent, and organizational-design research those claims usually skip. Three separate times, a claim of "unprecedented novelty" fails to survive contact with the actual evidence, while a narrower, more useful finding holds up again and again: oversight and accountability work when their form is real, not merely when they're present.
Built from decades of automation and human-factors research, principal-agent economics, job-design theory, and 2025–2026 findings on AI-agent oversight — checked against primary sources throughout, with what remains genuinely uncertain disclosed rather than smoothed over — the book culminates in the Load-Bearing Framework: a practical, evidence-grounded tool for deciding how much autonomy a specific AI agent needs, and what real accountability requires once it has it. A dedicated chapter scales the framework for organizations without a dedicated AI or platform team — the majority of real-world readers making this decision today.
What you'll find inside:
- Why the "AI delegation is unprecedented" claim doesn't survive scrutiny — and what actually is different (Chapter 1)
- Why "more human oversight is always safer" is false, and what the evidence says instead (Chapter 2)
- An honest staging of the industry's live debate over whether AI oversight is permanent or transitional (Chapter 3)
- What job-design research says about redesigning work around AI agents (Chapter 4)
- How many AI agents one person can actually oversee — and why "as many as you want" is wrong (Chapter 5)
- The Load-Bearing Framework: a complete, worked decision tool (Chapter 6)
- Whether "AgentOps Engineer" and similar titles are real new careers (Chapter 7)
- A full, honest catalog of what the research doesn't yet answer (Chapter 8)
- A dedicated version of the framework for organizations without a dedicated AI team (Chapter 9)
- A practical First-90-Days roadmap for applying all of it
Written for the business leader, operations executive, or department head deciding — this quarter — how much autonomy to grant an AI agent inside a real process. Not a technical book for AI engineers; a design book for the people responsible for the organization the agent works inside.
"Synopsis" may belong to another edition of this title.
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