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 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 a [...]
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