3624 software emulation, making them a drop-in modernization option for existing
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As AI agents transition into social settings, alignment challenges demand governance: actions that harm others need consequences – which requires people who can be held accountable. Kolt [114] draws on principal-agent theory to identify three core challenges: information asymmetry between agents and their principals, agents’ discretionary authority, and the absence of loyalty mechanisms. He argues that conventional governance tools face fundamental limitations when applied to systems making uninterpretable decisions at unprecedented speed and scale, and proposes technical measures, including agent identifiers, real-time surveillance systems, and logging. Our case studies make these challenges concrete: in Case Study #2, an attacker leverages information asymmetry to gain access to sensitive information, while in Case Study #1, the agent’s discretionary authority over the email server allowed a disproportionate response. Shavit et al. [115] enumerate seven operational practices for safe deployment, including constrained action spaces, human approval for high-stakes decisions, chain-of-thought and action logging, automatic monitoring by additional AI systems, unique agent identifiers traceable to human principals, and interruptibility—the ability to gracefully shut down an agent mid-operation.