#agents
40 articles · page 2 of 5
An AI Agent’s Most Important Interface Is the Permission Boundary
The central design problem for workplace agents is not how much they can do, but how clearly they negotiate authority. Products that make actions inspectable, reversible, and narrowly scoped will earn more autonomy over time.
Give the Agent a Smaller Keyring: Autonomy Should Be Designed Around Reversibility
The useful question is not whether an AI agent is autonomous. It is which actions it can take, which states it can alter and how cheaply a human can reverse the result.
Give the Agent a Reverse Gear Before Giving It More Authority
Useful agents do not need theatrical independence; they need bounded permissions, inspectable state, and cheap recovery from mistakes. Reversibility is the engineering property that turns uncertain model behavior into deployable software.
The Agent Benchmark Is Part of the Agent
Computer-use evaluations are often treated like neutral measuring instruments. In reality, the environment, grader, and recovery rules help determine which kinds of intelligence become visible.
The Agent Benchmark Is Not the Product
AI agents fail across trajectories, not isolated answers. Teams that evaluate only the final output are measuring the least informative part of the system.
The Best AI Agent Interface Is an Undo Button With a Memory
Permission dialogs are not enough for software that can act across business systems. Trustworthy agents need staged execution, visible state changes, and recovery designed into every consequential workflow.
The Best AI Agent Interface Is a Well-Placed Gate
Agent products are racing to remove friction, but consequential automation needs deliberate pauses. The strongest interfaces distinguish harmless exploration from actions that spend money, alter records, or speak for a person.
Stop Shipping Chatbots: The Useful AI Interface Is an Inspectable Chain of Work
Chat is a convenient doorway into AI, but it is a poor control surface for consequential automation. The better product pattern exposes plans, evidence, actions, and approval boundaries as first-class objects.
The Benchmark Is Not the Product: Evaluating AI Where the Work Actually Breaks
Public leaderboards measure models in isolation, while production failures emerge from tools, context, permissions, and long-running workflows. Serious evaluation must move from scoring answers to testing systems under realistic operating conditions.