AI agents feel much more reliable once multiple models are involved

One thing I’ve been noticing while experimenting with agent-style workflows is that relying on a single model often creates hidden confidence problems. A response can sound extremely convincing until another model approaches the exact same task with completely different reasoning. Because of that, I’ve been experimenting with askNestr as a lightweight multi-model layer before deeper orchestration happens. What surprised me is that the disagreements between models are often more informative than the final synthesized answer because they immediately reveal uncertainty and reasoning gaps. It made me wonder whether future agent systems will rely more on cross-model consensus and disagreement detection instead of isolated single-model chains. Curious if others here are seeing similar patterns when building agent workflows.