AI agents are learning on the job — just not for your whole team

When someone on a team corrects an AI agent — better prompts, better feedback, better context — that improvement disappears the moment a colleague opens the same tool. The correction doesn't transfer, and the next person starts from zero.The problem… When someone on a team corrects an AI agent — better prompts, better feedback, better context — that improvement disappears the moment a colleague opens the same tool. The correction doesn't transfer, and the next person starts from zero.The problem compounds in multi-agent workflows, where teams expect agents to share context across users and tasks. Without a shared memory layer, every team member effectively trains a different version of the same agent — and those versions never sync.That gap shows up in the numbers. According to Asana's own research, 75% of knowledge workers use AI on the job, but only 5% of companies have reported productivity gains. “Model providers are getting really, really good at improving reasoning and retry loops, but what they’re not good at is bringing the enterprise work context in a way that human beings can reason about for shared memory,” Asana Chief Product Officer Arnab Bose told VentureBeat. Asana had been building toward an agentic platform that centers…