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 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 context and shared memory. Its Agentic Work Management platform ensures that if any team member corrects an agent, that correction applies to everyone else on the team. “That context graph is automatically provided to agents operating inside Asana’s system so you don’t have to have every human member of the team become an expert at prompt engineering or context engineering,” Bose said. Bose said the shared memory architecture matters beyond Asana's own product; it's the design decision enterprises need to make for any multi-agent system.Shared memory also becomes important when enterprises begin moving from simple single agents to multi-agent workflows that need to share context and behaviors. Memories for a multi-agent, multi-platform workflowThe models powering agents are stateless by design, so memory becomes a dedicated layer outside of a context window. While this area of AI innovation is marching towards maturity, the question of what gets stored, who co