Last week, the AI industry advanced two different ideas about what a useful model should do. Meta wants an agent that remembers your constraints and keeps working when the first attempt fails. TypeSafe and Liquid want models that make a bounded decision without writing an essay about it. Somewhere between these ambitions is the software stack we actually need. A model that spends minutes repairing a codebase might be valuable. A model that spends minutes deciding which queue receives a support ticket has confused its job description. Intelligence has to fit the timescale and output format of the work. These releases make that engineering choice increasingly explicit. Meta builds models that stay with the task Read more
The Sequence Learning Loop - Issue #955: From Persistent Agents to Decision Models
Summary
Meta expands its agent models as Jev and Liquid sharpen structured decisions
The Sequence Learning Loop - Issue #955: From Persistent Agents to Decision Models
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