How to control the three unique categories of agentic AI risk
Summary
This whitepaper explains the three unique risks of agentic AI: lack of human oversight and accountability, goal misalignment, and amplification errors. It presents a dual-control framework that combines preventative controls with continuous detective monitoring. Key measures include restricted access, security filters, privacy protection, model testing, observability, and human intervention. The paper emphasizes that strong governance and technical testing are essential for deploying agentic AI safely and responsibly at scale.
Key Insights
- Agentic AI introduces three unique risk categories: lack of human oversight and accountability, goal misalignment, and amplification errors.
- Reduced human intervention can increase the risk of harmful or unethical outcomes going undetected.
- Automation bias can cause humans to place excessive trust in AI-generated decisions.
- Organizations must clearly define who is accountable when an agent makes a consequential decision.
- Goal misalignment can occur when an AI agent pursues objectives that differ from the organization's original intent.
- Goal drift, secondary uses, reward hacking, emergent behaviors, veiled objectives, and algorithmic determinism are important goal-misalignment risks.
- Amplification errors can occur when agents interact with one another or operate at scale.
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About the Author
Hammad Zubair
AI Transformation Leader | Founder of Zylo Technologies | Helping businesses unlock value through AI.
Hammad Zubair is an AI Transformation Leader and Founder of Zylo Technologies. He helps businesses discover practical AI opportunities that reduce costs, improve efficiency, and accelerate growth. Through AI readiness assessments and transformation strategies, he enables organizations to identify high-impact automation and AI implementation opportunities.