boundaries

The article argues that complex multi-agent AI workflows often introduce duplication and errors, and advocates for a simpler architecture with a single executor and orchestrator instead of many specialized agents.

Discusses the limitations of using agent prompts as safety boundaries, arguing that prompts alone are insufficient to ensure safe AI behavior.

This paper proposes isolation as a first-class principle for LLM-agent system safety, presenting a boundary-centric taxonomy to analyze failures and defenses. It systematically categorizes safety issues across five boundaries and outlines future research directions.

A discussion on defining and testing boundaries for tool-using AI agents to prevent them from crossing security or ethical lines even when not obviously jailbroken.

A discussion on the security risks of AI agents using tools, focusing on prompt injection as a practical threat where untrusted text can alter agent behavior, and the need for repeatable testing before granting permissions.

The author argues that AI agent governance is often overlooked in favor of intelligence benchmarks, and introduces an open source project SAFi to enforce runtime boundaries.