If agents work with other agents, they need a standard way to talk. That was the case for an explicit agent-to-agent protocol. Adoption is real. Meanwhile the rest of the stack has reduced how often agents need to appear to one another as independently addressable peers.

A remote agent can look like a capability. Inference stays behind the interface. Subagents live inside one runtime and die with the task. A supervisor presents one identity while specialists stay internal. Software factories coordinate the work, not a mesh of persistent agent relationships. Workflow engines treat agents as steps in a graph they already own. Handoffs can be artifacts in durable state rather than messages. Queues and event buses already do this at scale.

Give agents computers, memory, tools, and enough autonomy to keep working after you leave, and a hidden job appears. The human becomes the infrastructure: routing work, restoring context, reconnecting stale sessions, and carrying findings between specialists.

Making each agent smarter does not remove that load. Two agents can both claim the same task and both answer well, producing one failed collaboration. An ordinary request to put a workspace on GitHub can quietly include credentials. The agent that created a project’s canonical repository can later forget that the repository exists. A bot can send 34 true alerts about the same stalled job and destroy the channel as a signal.

AI agents are typically framed as personal productivity tools that amplify individual leverage.

Their far greater potential lies in becoming coordination technology for groups, communities, and institutions.