this piece is worth the read, but quite dense. here's my (relatively) brief breakdown: super agents are defined by 4 characteristics: - continuous: one agent across threads and access points - productive: responds quickly and completes work efficiently - proactive: responds to events in its environment - persistent: durable execution for high availability and crash recovery these constraints apply to personal agents and agents doing commercial work alike, so the underlying architecture should look similar the primitives behind them: - two level architecture: a router layer keeps interaction continuous, and a worker layer runs tasks concurrently - event driven design: the agent reacts to events in its environment and can schedule runs toward a long term goal - multi channel interaction: context carries across channels, and async execution keeps the agent productive - separate compute, state, and environments: the "separate the brain from the hands" idea, which supports persistence and security and lets the system scale - watchdog: an independent control loop that keeps the whole thing running they're upfront about what's still open: - reliable scale - representing tasks and work without the "session" primitive - agent to agent communication and context standards - security and privacy, as always their core claim is that models need less and less human intervention on individual tasks, so the bottleneck to further productivity gains is shifting to human attention which raises the question i'm most interested in: how do we design human-agent collaboration when human attention is the bottleneck?