OpenThoughts-Agent: Data Recipes for Agentic Models

I see AI agents everywhere now, from Instacart to Confluence to banking apps, and I was curious to know how they actually work behind the scenes. Are they mostly built on the exact same underlying models, or does each company code their own? If multiple companies use the same model, what stops them from acting exactly the same? Also, do they all benefit when the base model gets updated in the case

Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to train models that generalize across diverse agentic tasks. The OpenThoughts-Agent (OT-Agent) project

I’m not an AI researcher, so this may be a naive question. I asked how future AI systems will learn tasks where there simply isn’t enough training data available. Someone responded that “agents will solve that problem.” I’m confused by this answer. My understanding is that agent frameworks are mostly a software layer around foundation models (planning, tool use, memory, workflow orchestration, etc