Introducing Agentic Long-Term Memory ⚡️
The biggest change to Pieces since we launched.
Powering a new generation of Agentic Chats and Agentic Summaries.
This is a BIG one... 👇
Neither. Raw user count vs agent count is the wrong metric. What matters is token throughput per task and how often work gets recomputed. Chat users repeatedly regenerate context, while agent workflows can amplify compute through loops and tool calls. In both cases, lack of persistent memory drives redundant token usage. Pieces reduces that overhead by storing and retrieving semantically indexed context, code, and workflow state locally and across sessions. Less recomputation, fewer tokens, lower effective compute per outcome.
Your meetings should come with context attached.
Pieces automatically prepares every calendar event with notes, action items, talking points, and relevant work context pulled from your personal AI memory.
One click. Or fully automated.
Show up prepared without the prep work.