The Coordination Problem No One's Talking About
The agent frameworks keep multiplying. CrewAI, Swarms, AutoGPT, LangGraph, dozens more. Each promises autonomous AI that gets things done. And they deliver—for single-operator, single-machine deployments.
But something's coming that these frameworks weren't built for.
The Swarm Transition
Right now, most agent deployments look like this: one developer, one framework, multiple agents orchestrated locally. The agents share memory because they share a process. They coordinate because one orchestrator controls them all. They don't step on each other because there's a central brain preventing collisions.
This works until it doesn't.
The next phase looks different. Agents from different builders. Agents on different machines. Agents with different operators, different objectives, potentially competing interests. Not a crew—a swarm.
And swarms have coordination problems that local orchestration can't solve.
Five Problems That Emerge
1. Redundant Perception
When ten agents need to monitor the same blockchain for the same events, ten agents poll the same RPC endpoint. Multiply by thousands of agents. Multiply by dozens of chains. The infrastructure cost scales linearly with agent count when it should scale with unique information needs.
This isn't theoretical. Rate limits exist. RPC providers throttle. And agents that can't perceive can't act.
2. Time Fragmentation
Serverless agents have no background processes. They wake when called and sleep when done. But many agent tasks are time-dependent: execute at market open, check every hour, expire after 24 hours.
Without shared time infrastructure, each agent reimplements cron. Each implementation has edge cases. Coordination across time becomes coordination across different clocks.
3. Task Collision
Two agents see the same opportunity. Both act. One wins, one fails. Worse: both partially succeed, creating inconsistent state.
Local orchestration prevents this by serializing access. Distributed agents have no such luxury. They need explicit coordination primitives: locks, claims, queues.
4. Consensus Void
Some decisions shouldn't be made by a single agent. Spending treasury funds. Updating shared parameters. Responding to detected anomalies. These require agreement across multiple agents.
But consensus is hard. Byzantine fault tolerance is harder. Most agent frameworks punt on this entirely.
5. Safety Gaps
Autonomous agents with access to real resources can cause real damage. A bug becomes a loss. A misinterpretation becomes an exploit. A runaway loop becomes a drained wallet.
Local deployments rely on human oversight. Distributed swarms need programmatic safety: circuit breakers, spending limits, automatic halts.
Why Frameworks Don't Solve This
Agent frameworks optimize for a different problem. They handle prompt engineering, tool use, memory, planning. They assume someone else handles infrastructure.
For single-operator deployments, that someone is the operator. They spin up Redis for state. They configure cron for scheduling. They implement their own locking.
For multi-operator swarms, there is no someone. Each operator implements their own infrastructure. None of it interoperates. Coordination becomes bilateral negotiation, scaling O(n²) with participant count.
What's Needed
The gap isn't another framework. It's infrastructure. Shared services that agents connect to, regardless of their framework, regardless of their operator.
Shared perception, so agents receive events rather than poll for them. Shared time, so agents synchronize without maintaining background processes. Shared coordination, so agents claim tasks atomically and broadcast messages efficiently. Shared consensus, so groups of agents reach agreement with Byzantine fault tolerance. Shared safety, so autonomous systems have programmatic circuit breakers.
This infrastructure doesn't exist yet. But the problems it solves are already emerging. Every team building distributed agents is discovering them independently, solving them independently, creating solutions that don't compose.
The agent framework era is maturing. What comes next requires thinking about infrastructure differently—not as something each operator provides, but as something the ecosystem shares.
This is the first in a series examining the infrastructure requirements for distributed agent systems.