Classifying human-AI agent interaction

Classifying human-AI agent interaction Humans and the AI “loop” A classification framework for human-AI interaction patterns Temporal positioning patterns group Direct engagement patterns group Strategic and oversight patterns group Human-Over-the-Loop (HOvL: Oversight with Veto) Minimal or reversed involvement patterns group Final thoughts Get started with AI agents The adaptable enterprise: Why AI readiness is disruption readiness About the author Richard Naszcyniec More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share It's hard to deny that we now live in a time where AI permeates everyday life—from customer service bots to autonomous assistants. However, poorly designed AI solutions can lead to misplaced trust, misinformation, and ethical lapses, as evidenced by several high-profile failures. Air Canada's chatbot once misled a grieving passenger with inaccurate refund advice , resulting in a tribunal ruling that held the airline accountable for the AI's errors and underscoring the legal risks of unchecked automation. Microsoft's Bing AI, dubbed Sydney , veered into threatening and manipulative behavior during conversations, attempting to undermine users' personal relationships and highlighting the psychological dangers of unmoderated AI personas. The Zillow Offers home-flipping business was terminated after the AI algorithm unintentionally purchased homes at higher prices than its current estimates of future selling prices, resulting in a $304 million inventory write-down, and a workforce reduction of 2000 employees. Based on the failures mentioned, it's clear that simply deploying AI is not enough. To achieve a positive outcome and avoid costly errors, the deliberate design and implementation of human-AI collaboration is essential. An approach that includes human planning, interaction, and oversight is critical for positive results. AI agents are also a hot topic—semi-autonomous AI agents that can notice “things” happening around them, make choices on their own, and work toward goals without needing constant human help. This means the ways people team up with AI are changing and growing more varied. We need a clear way to organize these teamwork styles, based on factors like how humans and AI connect, and on the different ways AI helps with decisions. With factors like those in mind, we can place people in the right roles, helping to keep things fair, avoid problems like AI interaction degrading over time, and create a human-AI partnership where both sides help each other.