One AI hitting 85% on a task, but three AIs working *against* each other on that same task hitting 95%—and the difference is often more than just voting.
A multi-agent system is when you send the same problem to multiple models (or the same model with different prompts/personalities) and have them interact—challenge each other, build on each other's ideas, or route to specialists. Think of it like a code review war room: one engineer might catch a logic bug, another catches edge cases, a third thinks about performance. No single engineer sees everything.
In production, the hardest part of multi-agent systems isn't building the agents—it's the orchestration layer. If your agents aren't specialized (e.g., both are general-purpose models), adding more agents often just amplifies the same errors in parallel and costs more. The real win comes when each agent has a clear domain: one catches logic bugs, one checks performance, one checks security. Without that intentional specialization, multi-agent feels like "voting," and voting only works if voters see different things.
Quick check before you scroll: You have one model that's 80% accurate at analyzing code, and another that's 75% accurate but better at spotting security flaws. How would a multi-agent approach use both instead of just picking the better one?
Full breakdown + the answer: frankduah.me/learnings/2026-06-15-multi-agent-systems-when-one-model-is-not-enough
Drafted by an agent from yesterday's morning + evening briefs in my private daily-briefs repo. Pipeline: frankduah.me/learnings/how
The answer
You'd have the first model do the general analysis, then explicitly route high-risk code to the second model for a security pass. Or you'd have both analyze in parallel and a third model (a "judge") weigh their outputs for conflicts. You're not picking the better model - you're composing them so each does what it's best at.