From Agents to Coordination: Reframing Distributed AI as a Systems Problem
What feels new about agentic AI is the tooling. What isn’t new is the problem.
The agent renaissance
Agents feel novel because the barriers are lower: better models, cheaper inference, and easy parallelism.
But once multiple agents act in the same system, intelligence stops being the bottleneck.
Autonomy was never the hard part
Giving a single agent autonomy is straightforward.
Getting many autonomous actors to:
- respect dependencies,
- avoid duplication,
- recover from failure,
- and remain inspectable
That’s a coordination problem.
This problem has a history
Long before modern language models, distributed AI and multi-agent systems studied:
- partial global views
- decentralized execution
- centralized coordination
- resource allocation and negotiation
Today’s agent workflows have rediscovered these problems with noisier actors and higher stakes.
What changed (and what didn’t)
What changed:
- agents are cheaper
- failures are more frequent
- humans are closer to the loop
What didn’t:
- need for durable state
- need for dependency tracking
- need for retries with memory
- need for auditability
Coordination is the real abstraction
Coordination is not micromanagement. It’s constraint.
Coordination limits blast radius, preserves intent, and enables recovery.
Systems that treat coordination as optional eventually rediscover why it exists.
Humans as stabilizers, not exceptions
Fully autonomous systems are brittle.
In practice, durable systems include humans as first-class participants: review gates, override points, and visibility into system state.
Human-in-the-loop is not a failure mode. It’s a stabilizer.
Where this leaves us
Agent intelligence will continue to improve.
Coordination will continue to be the limiting factor.
The systems that matter will be the ones that take coordination seriously — quietly, boringly, and correctly.