I spent the first decade of my career building monoliths. Then microservices came along and everything changed. We decomposed our giant applications into small, focused services that did one thing well and communicated over well-defined interfaces.
Now I'm watching the same pattern emerge with AI — but this time, the decomposition is even more profound.
The Microservices Parallel
When microservices first became popular, the promise was elegant: small teams could own small services, deploy independently, and scale horizontally. The architecture enabled organizational scaling.
But there's a hidden cost nobody talks about enough: the complexity of orchestration. Service discovery, distributed transactions, network reliability, monitoring — suddenly you needed an infrastructure team just to manage the communication between services.
AI agents are hitting the same inflection point.
Agents as Decomposition Units
Just as a microservice encapsulates a business capability, an agent encapsulates a capability enhanced by AI. But here's what's different: agents don't just expose APIs. They maintain state, handle context, make decisions, and learn.
The unit of decomposition is no longer a function — it's a capability that includes the judgment to use it.
At Orochi, we've started thinking about our products as collections of agents. Bifrost isn't one application — it's a language learning agent, a conversation facilitator, a progress tracker, and a motivation agent working together.
The Orchestration Problem
This is where it gets interesting. Just as we needed Kubernetes to manage microservices, we need something to manage agents.
The MCP (Model Context Protocol) is early but promising. It provides a standardized way for agents to communicate, share context, and coordinate. Think of it as service mesh for AI — the infrastructure layer that makes agent composition possible.
But we're still early. The patterns aren't established yet. We're all experimenting.
What This Means for Builders
If you're building AI products today, think about agent architecture:
- Single-responsibility agents: Each agent should do one thing well
- Well-defined interfaces: How do agents request help from other agents?
- State management: Agents need memory — what's shared, what's private?
- Failure modes: What happens when one agent fails?
This is the frontier. The patterns we establish now will shape AI architecture for years.
The Bottom Line
Microservices taught us that decomposition enables scaling — of teams, of systems, of organizations. AI agents extend this to include the decomposition of intelligence itself.
The future isn't one smart system. It's many focused agents, working together, each expert in their domain.
That's the future we're building toward.
The best systems are those that know how to ask for help.