I built an open-source agent whose reasoning core fuses several LLMs (panel, judge, synthesizer) instead of routing to one

Most agent frameworks pick one model per call. I wanted to test a different idea: for the hard steps, run a panel of different models on the same prompt, have a judge model cross-check them (consensus / contradictions / blind spots), then a synthesizer writes the final answer. A cost-aware router keeps easy and tool turns on a single fast model and only fuses when it's worth it. Around that core I built the rest of a real agent: plan -> act -> verify-or-revert (executable evidence is the ground truth, so a strict reviewer can't discard verified-correct work), layered memory (full-text recall + a cross-session user profile + LLM consolidation of fact clusters), a governance kernel (allow/warn/block/review + a static validator for self-modification), cron and proactive jobs, an MCP client + OpenAPI-to-tool import, and an isolated subagent/crew layer that runs workers in parallel git worktrees with per-worker verify gates. Honest status: it's alpha - Apache-2.0, self-hostable, 463 tests, mypy --strict clean - so it builds and is heavily tested, but it has no production mileage yet. What I'm genuinely unsure about and would love this sub's take on: is fusion (panel -> judge -> synthesizer) actually worth the extra tokens and latency versus just calling one strong model? My own benchmarks are mixed - it clearly helps on ambiguous, open-ended reasoning, but on well-scoped coding tasks a single top model often matches it for a fraction of the cost. Where have you found multi-model setups actually pay off? (Repo link in a comment, following rule 3.)