Every multi-agent system I've built started as a monolith: one agent, one runtime, one set of tools. It works for the demo. Then you add a second agent, then a third, and suddenly every failure brings everything down.
The instinct is to fix the agents. Better prompts, more compute, smarter models. But the real issue isn't the agents. It's the architecture underneath them.
After building production multi-agent systems that actually survive contact with real workloads, I landed on a four-layer model that keeps the pieces independent and the failures contained.
The four layers
Agents orchestrate the work. Each agent has a harness (instructions and tools), a runtime (where it runs), an owner (who maintains it), and a clear definition of its role and boundaries.
Models provide intelligence. Not every agent needs the most expensive model. A coordinator that plans complex tasks might use a frontier model, while a reporter that formats output does not. Model routing and fallback chains cut costs by an order of magnitude.
Tools extend what agents can do. Built-in tools give agents basic capabilities. External tools on MCP servers give them access to databases, APIs, and services. Tool governance ensures agents only call what they're authorized to call.
State is the memory. Each agent owns its own state partition. No agent writes to another agent's partition. Shared-nothing ownership means failures stay isolated.
The layers connect through protocols like A2A (Agent2Agent) for inter-agent communication and MCP for tool discovery. On Azure, Foundry combines the runtime, harness, tools, and orchestration into a single managed service, but the architecture itself is platform-agnostic.
The key insight: not every system needs all four layers. Some solutions use two. Some use three. Patterns are what combine the components into something that works in real life.
Want the complete architecture? Read the complete post at zulmehdi.blog (https://zulmehdi.blog/4-layer-multi-agent-enterprise-architecture-azure/).
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