Three AI agents can produce different proposals and still miss the same underlying mistake. In one workflow for rewriting quiz questions, an explicit “refuter” caught a proposal that confused a denominator with a subset—an error three proposal agents and the author had initially missed. The more important lesson was that the team had also assumed its measured metric matched the operator’s reported problem, when they were actually different.
What the five-agent workflow did
In a September 1, 2026 account, Renga describes using five AI agents while considering how to rewrite 671 quiz questions. Three agents proposed approaches from different perspectives, a fourth was asked to challenge a proposal, and a fifth synthesized the outputs. These counts and events are the author’s account, not independently audited figures or the result of a controlled evaluation. Read Renga’s account on DEV Community.
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The refuter’s instruction was: “Find the place where you can say ‘this will fail in execution’”. It looked for assumptions and numerical claims that had not been checked by running the relevant process. In one case, it rejected a proposal because a headline number treated a subset as the denominator. The three proposal agents and Renga had initially missed that error.
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Renga’s concise distinction captures the limitation: “Splitting the perspectives buys you independence of perspective. It does not buy you independence of assumption.” Different wording or angles do not guarantee that agents start from different premises.
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Check whether the metric describes the actual problem
The arithmetic error was visible, but Renga identifies a deeper shared blind spot: the workflow assumed that the metric being measured was the same thing the operator said was going wrong. When asked, the operator described a different problem. A correct calculation can still lead to the wrong rewrite if the team is measuring the wrong thing.
Before asking agents to optimize a metric, confirm what the people affected actually find confusing or broken. Renga reports that the synthesizer had to return an open_question field; the question that came back was to ask the operator which items they actually found confusing. Treat that as a useful prompt for clarification, not as proof that one question will resolve every problem definition.
Make numerical claims reproducible
Renga recommends giving agents the same measured data in a file so that a disagreement can be checked against a common source. A shared input does not make every recount correct: the account says one agent still miscounted. It does make it easier to locate the discrepancy and compare the answer with a reproducible basis.
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- Have numbers come from running a script or other defined process, rather than estimating or recounting by eye.
- Give each agent the same generated data when their work depends on counts or assignments.
- When outputs disagree, compare them with the source data and rerun the calculation instead of choosing the most plausible-looking number.
The account also describes a handoff failure: after a script assigned 116 sites across five agents, Renga copied the assignments into JSON by hand and later found 28 incorrect assignments. Those figures are reported by the author, not independently verified. The practical fix is to remove manual transcription where possible; otherwise, diff the handoff against the generated assignment and check names against the actual files.
Set quality rules before distributing work
Renga says they first completed 76 rewrites and wrote a quality standard before distributing another 390. The figures describe this particular effort, not a general productivity benchmark. The underlying practice is to define what counts as an acceptable rewrite before parallel work begins, so agents have a concrete target rather than an implicit standard inferred from examples.
Keep machine checks as the release gate where applicable. A fluent proposal is not evidence that it satisfies the requirements: checks should validate the properties they can actually test, while a human or operator resolves questions that require context or judgment.
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Keep challenge and synthesis separate
A synthesizer can accidentally restore a flaw by blending a proposal that the refuter rejected with a stronger alternative. Renga warns against treating “the best of both” as automatically safe when one proposal’s underlying assumption has already been challenged. Preserve the rejected proposal’s failure reason through synthesis and require the final output to address it explicitly.
A refutation should also be open to scrutiny. In discussion attached to Renga’s post, a commenter argues that a refuter should cite the specific evidence behind its objection. That is a suggestion in the discussion, not a tested formal method, but it points to a useful safeguard: ask the refuter to identify the claim, the evidence or calculation that contradicts it, and what would change its conclusion.
What this account can—and cannot—show
Renga also recounts a separate video-cutting example in which six agents encountered a missing font file and solved the issue in different ways. That is another anecdote, not comparative evidence about agent reliability.
The quiz-rewriting account offers concrete ideas for structuring work: share source data, validate the problem with the operator, require unresolved questions to be surfaced, write a quality standard, and challenge proposals before synthesis. It does not report a controlled comparison, a measured error rate, or evidence that five agents—or this particular division of roles—will outperform another workflow. The useful comparison is therefore practical rather than statistical: adding more proposal agents creates more candidate approaches; adding a refuter creates an explicit check on assumptions. Either can help only if inputs and claims can be verified.
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