When Federation vs Centralization Makes Sense for AI Agents
Federation vs Centralization for AI Agents
The idea of federated AI agents sounds elegant - independent agents coordinating across organizations, each maintaining their own data and logic while collaborating on shared goals. The reality is that federation adds coordination costs that most teams underestimate.
The Coordination Tax
Every time two federated agents need to share state, you pay a tax:
- Schema alignment - both agents need to agree on data formats
- Conflict resolution - when agents disagree, something needs to arbitrate
- Latency - coordination requires round trips that centralized systems avoid
- Partial failure - one agent going down affects the entire federated workflow
In financial AI workflows, these costs compound. A federated system where three agents manage portfolio, risk, and compliance needs constant synchronization. Every trade triggers coordination across all three. The overhead can exceed the cost of running a single well-structured agent.
When Centralization Wins
Centralize when:
- One team controls all the agents
- Data can live in one place without regulatory issues
- Latency matters more than independence
- The system is small enough that one agent can handle the full context
Most agent systems are smaller than their architects think. A single agent with good tool access often outperforms three federated agents that spend half their tokens coordinating.
When Federation Actually Makes Sense
Federate when:
- Data legally cannot leave certain boundaries
- Different organizations own different parts of the workflow
- Agents need to operate independently during network partitions
- Scale genuinely exceeds what one system can handle
The Default Should Be Simple
Start centralized. Add federation only when a specific constraint forces it. The complexity cost of premature federation is far higher than the migration cost of adding it later.
Fazm is an open source macOS AI agent. Open source on GitHub.