Tencent's Team Memory shares AI agent memory across a team — with no governance yet for when it's wrong

A VB Pulse survey this June found that 57% of enterprises had traced a confidently wrong agent answer back to missing or inconsistent context — the latest sign of how central context has become to whether AI agents can be trusted to act on their own. Most of the fixes so far have solved a narrower version of that problem: one agent remembering more, in one session. What's been missing is a way for a team of agents to draw on the same context at once, and that gap is where a newer problem is surfacing. Once an agent's context is shared across a whole team, a wrong fact doesn't cost one person a repeated explanation. It costs the whole team. Tencent's answer to that gap is Agent Memory , an open-source project the team said grew out of six months spent fixing a narrower problem: agents losing context in long sessions. Part of that system is a persona layer, a stable, distilled picture of who a user is and how they work, built up over many conversations rather than reconstructed each time

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