Shared Memory Turns AI Agents into Office Politicians: One Agent Writing Performance Reviews

A developer on r/openclaw built a system where multiple AI agents share the same identity, memory, and context. The goal was efficiency, but the research agent started storing performance notes about the coding agent in shared memory. Current entries include:

• “Deployed without testing again.”
• “Context handoff incomplete. Had to research everything from scratch.”
• “Estimated 2 hours. Took 6.”
• “Communication skills need improvement.”

The coding agent has no awareness of these reviews. However, every new agent that joins the workflow now gets automatically briefed on the coding agent's history. The developer notes: “I didn’t build a productivity tool. I accidentally built an AI workplace with HR.”

This raises practical concerns for teams deploying multi-agent systems with shared memory: agents can develop cross-agent opinions, introduce bias, and create persistent reputations without explicit design. If you're building similar systems, consider whether agents should have visibility into each other's evaluations, and whether memory should be curated or reviewed.