Practical Multi-Agent System Architecture Advice from Experience

A developer on r/openclaw shared practical advice for architecting multi-agent AI systems based on experience building a 7-agent system that runs daily. The advice comes from helping another developer who was stuck on architecture decisions while building a content automation pipeline.

Key Architecture Patterns

The developer outlines five specific approaches that work in practice:

• Start with one agent: Don't begin with multiple agents. Get one agent working first, understand it, then add a second only when the first hits a wall it can't solve alone. Most businesses need 2-4 agents maximum - a barber automation system mentioned runs on 4 agents.
• Use the orchestrator pattern: One agent that sees everything and routes work to specialists. Not a democracy or round-robin approach - "one brain, multiple hands."
• Implement shared memory with JSON files: Agents that can't see each other's work will duplicate, contradict, and waste tokens. The solution is a shared-brain directory using JSON files that every agent reads before starting and writes after finishing. Simple approach - no database or vector store needed.
• Route models by task: Not every agent needs expensive models. The developer's content agent runs on Sonnet, research agent runs on a free model, while only the orchestrator and high-task operators get expensive models. This approach can save 80% of budget.
• Add confirmation loops: Every agent posts its work to a channel. The orchestrator reviews - if it passes, it ships; if not, it goes back with notes. Nothing leaves the system without a check.

Practical Implementation

The core insight is avoiding upfront over-engineering. The developer who requested help was stuck because he was trying to design the whole system at once. Instead, the advice is to build one agent, solve one problem, then add the next agent only when the first proves it works.

The shared memory approach using JSON files provides a lightweight solution for agent coordination without complex infrastructure. Model routing by task specificity helps control costs while maintaining performance where it matters most.