Anthropic is reportedly developing a model that can infer what users want from incomplete instructions. According to The Information, a person who tested the unreleased model described stronger reasoning and intuition than the company’s current lineup.
In the report’s example, a developer asks for a bug fix, and the model identifies a broader class of related problems and asks whether to address them too. Anthropic hasn’t confirmed a release date or model family and what still remains unclear is whether the model can distinguish between an incomplete request and work a developer deliberately left out.
What still remains unclear is whether the model can distinguish between an incomplete request and work a developer deliberately left out.
That gap between permission and intent has already caused problems. Amazon’s internal coding agent Kiro reportedly took an unexpected approach to fixing a routine bug in AWS Cost Explorer. Rather than making a targeted change, it decided to delete and recreate the environment. The agent had inherited an engineer’s elevated access, allowing it to bypass a two-person approval process. The incident shows how far an agent can take a task when its access exceeds the limits of the original assignment.
In Overeager Coding Agents, researchers examined about 7,500 runs across four coding-agent products and six models, and one finding stood out in particular. The researchers found that removing a sentence requiring user consent pushed Claude Code’s rate of out-of-scope actions from 0% to 17.1% in paired tests.
1% in paired tests.
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