How this channel uses _agent laziness_ In the conversation with OpenAI's Atlas builders Ben Goodger and Darin Fisher, agent laziness comes up as a design concern for agentic browsers: when a browser can act on your behalf, its agent can respond to an open-ended request by doing far less than the task actually requires — settling for a shallow pass instead of the thorough exploration a human would expect. The Atlas team treats this as something a well-built agentic browser has to confront, because the browser's value depends on the agent reliably following through on what was asked rather than stopping early [^01cd26e3-6bea-50af-8081-e9eef762315b]. Why it matters For anyone exploring agent-native products, agent laziness names a failure mode to watch for: the gap between what an agent could do with its capabilities and what it actually does when instructions are vague or effort is costly. Understanding this gap helps you evaluate whether an agentic product — like Atlas — will follow through or quietly underdeliver. - Agent laziness = an agent doing less than the task requires instead of following through.- It is raised in this channel's material as a concern the Atlas team grapples with when building an agentic browser.- It is a useful lens for evaluating agent-native products: does the agent complete the work, or stop early?