We built our first "multi-agent system" by accident. What started as a single agent that could research a topic, draft a report, check it against source data, and send a summary email had grown into a 2,000-token system prompt and a function list so long that the model kept forgetting tools existed. It wasn't a system — it was a monolith pretending to be intelligent.
Breaking it apart into coordinated agents fixed most of the problems. It also introduced a new category of problems we hadn't thought about. Here's what we actually learned.
When One Agent Is Enough (and When It Isn't)
The temptation to add more agents is real, but the overhead isn't free. Every agent boundary you add is a place where context can get lost, latency increases, and errors compound.
One agent is the right call when:
