The microservices analogy is surprisingly accurate — and I think it extends further than most people realize. Just like microservices, multi-agent systems go through the same maturity curve: monolith → decomposition euphoria → "oh no, distributed systems are hard" → settling on practical boundaries.
The biggest lesson from the microservices era that applies directly: don't decompose too early. Teams see multi-agent demos and immediately want to split everything into 8 specialized agents when a single agent with good tool selection handles 90% of use cases. The coordination overhead between agents is real — context loss at handoff boundaries, duplicated reasoning, cascading failures. Sound familiar to anyone who over-decomposed into microservices?
Where multi-agent genuinely shines is when you have tasks that benefit from different model strengths. Routing a research task to a model with strong retrieval capabilities and a code generation task to a coding-specialized model — that's the equivalent of "right tool for the right job" that made microservices worthwhile.
What patterns have you seen for managing shared state between agents? That's the hardest infrastructure problem and it's where the analogy to service meshes gets really interesting.
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The microservices analogy is surprisingly accurate — and I think it extends further than most people realize. Just like microservices, multi-agent systems go through the same maturity curve: monolith → decomposition euphoria → "oh no, distributed systems are hard" → settling on practical boundaries.
The biggest lesson from the microservices era that applies directly: don't decompose too early. Teams see multi-agent demos and immediately want to split everything into 8 specialized agents when a single agent with good tool selection handles 90% of use cases. The coordination overhead between agents is real — context loss at handoff boundaries, duplicated reasoning, cascading failures. Sound familiar to anyone who over-decomposed into microservices?
Where multi-agent genuinely shines is when you have tasks that benefit from different model strengths. Routing a research task to a model with strong retrieval capabilities and a code generation task to a coding-specialized model — that's the equivalent of "right tool for the right job" that made microservices worthwhile.
What patterns have you seen for managing shared state between agents? That's the hardest infrastructure problem and it's where the analogy to service meshes gets really interesting.