You are assuming statistical independence, which is explicitly not correct here. There is also an error in your analysis - what matters is whether they make the same wrong assumption. That is far less likely, and becomes exponentially unlikely with increasing trials.

I can attest that it works well in practice, and my organization is already deploying this technique internally.

▲ thesz an hour ago | parent [-]

How several wrong assumptions make it right with increasing trials?

▲ adastra22 an hour ago | parent [-]

You can ask Opus 4.6 to do a task and leave it running for 30min or more to attempt one-shooting it. Imagine doing this with three agents in parallel in three separate work trees. Then spin up a new agent to decide which approach of the three is best on the merits. Repeat this analysis in fresh contexts and sample until there is clear consensus on one. If no consensus after N runs, reframe to provide directions for a 4th attempt. Continue until a clear winning approach is found.

This is one example of an orchestration workflow. There are others.

	▲	thesz 22 minutes ago \| parent [-]
		`> Then spin up a new agent to decide which approach of the three is best on the merits. Repeat this analysis in fresh contexts and sample until there is clear consensus on one.` If there are several agents doing analysis of solutions, how do you define a consensus? Should it be unanimous or above some threshold? Are agents scores soft or hard? How threshold is defined if scores are soft? There is a whole lot of science in voting approaches, which voting approach is best here? Is it possible for analyzing agents to choose the best of wrong solutions? E.g., longest remembered table of FizzBuzz answers amongst remembered tables of FizzBuzz answers.