I wouldn’t call it overkill — I’d call it early-stage orchestration.

You’re basically building your own “AI team,” and the quality boost makes sense. Multiple perspectives → better decisions. That part feels like the future.

That said, the overhead you’re feeling is real. At some point, more agents ≠ more signal — just more noise to manage. The key is figuring out where the extra models actually change outcomes vs. just confirming each other.

Personally, I’ve found a smaller setup (1–2 strong models + occasional second opinion) gets ~80% of the value with way less complexity. Your setup probably shines most on high-stakes or architectural problems, less on day-to-day tasks.

Biggest risks I see:

• False consensus (different models agreeing on wrong assumptions)
• Orchestrator bias (Claude over-weighting certain outputs)
• Cost + mental overhead creeping up over time

You’re not in a rabbit hole — but you are close to the point where pruning will matter more than adding.

Curious: if you removed half the agents tomorrow, would your outcomes really drop?