Can AI Agents Check Each Other's Work? Multi-Agent Setup
The single trick that raises AI quality most: don't trust one answer. Have a second agent challenge it, try to break it, and surface what the first one missed.
Evgenii Arsentev · PhDReviewed for accuracy by Evgenii Arsentev, PhD · 2026-06-15
Here's the single move that raises the quality of AI work more than any clever prompt: stop trusting one answer. Have a second agent independently check it — challenge it, try to break it, hunt for what's wrong. One AI can be fluent and confidently mistaken; a second one whose only job is to disagree catches what the first one glided past. This is how you get expert-level reliability without being an expert yourself.
Why one AI answer isn't enough
An AI always sounds sure of itself — even when it's wrong. It can invent a fact, miss an edge case, or 'fix' a bug that's still there, and report all of it in the same confident voice (that's the hallucination problem). If you act on the first answer, you inherit those mistakes. The fix isn't a better single answer — it's a second opinion.
What 'agents challenging each other' means
You let one agent do the work, then bring in a FRESH agent with a different job: not to be helpful, but to be skeptical. 'Here's a solution — find every flaw in it. Assume it's wrong until proven right.' Because the second agent didn't write the first answer, it has no pride in it, so it pokes holes the original would defend. Weak answers fall apart under that pressure; strong ones survive it.
No serious article ships straight from the writer. An editor — a second pair of eyes whose whole job is to find problems — reads it cold and tears into the weak parts. Same words, sharper result. Making two agents writer-and-editor does exactly this, in minutes, for whatever you're building.
Three ways to make them challenge each other
Pick the pattern that fits
- 1VERIFY: one agent solves it, a second checks every claim — 'try to refute this; flag anything you can't confirm.' Best for facts, research, and 'did it actually fix the bug?'.
- 2DEBATE: two agents argue opposite sides ('this approach is best' vs. 'no, this one is'), then a third reads both and decides. Best for choices and trade-offs.
- 3VOTE: three agents solve the same thing independently, and you take the answer they agree on. Independent tries rarely share the same mistake, so agreement is a strong signal.
Two reasons. First, independent agents rarely make the SAME mistake — so a disagreement is a flashing light pointing right at the weak spot. Second, framing matters: an agent told 'check this' is polite and tends to approve; an agent told 'try to REFUTE this, default to wrong until proven right' actually digs. Skeptical framing turns a rubber stamp into a real review.
How do I ask for it?
Solve this, then start a fresh agent whose only job is to find every flaw in that solution — tell it to assume the answer is wrong until proven right, and report which problems are real.
You don't referee it yourself. You describe the roles — solver, then skeptic — and Claude runs both and hands you the verdict. Add a third 'judge' agent when you want a final call between competing answers.
This uses more passes than a single answer — but on a personal subscription that's already included (see the 'Run 10 AI Agents at Once' guide), so quality is basically free. Two limits to respect: a challenge round lowers risk, it doesn't erase it — for anything high-stakes you still review the final result yourself; and don't bother for tiny, obvious tasks where one clear request is enough.
Why does having a second agent challenge the first improve quality?
Never ship the first answer. Make one agent do the work and another tear into it — verify, debate, or vote. It's the closest thing to hiring an expert reviewer, it costs you nothing extra on a subscription, and it turns 'probably right' into 'actually checked'.
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Selected peer-reviewed papers and preprints on the AI research behind this topic.
- [1]Zhu et al. (2022). A Survey of Multi-Agent Deep Reinforcement Learning with Communication. arXiv:2203.08975
- [2]Soyez et al. (2013). A Methodology to Engineer and Validate Dynamic Multi-level Multi-agent Based Simulations. arXiv:1311.5108
- [3]Peters et al. (2024). Emergent Language: A Survey and Taxonomy. arXiv:2409.02645
- [4]Takayama et al. (2025). AOAD-MAT: Transformer-based multi-agent deep reinforcement learning model considering agents' order of action decisions. arXiv:2510.13343
- [5]Bredell et al. (2024). Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi. arXiv:2412.06333
- [6]Li et al. (2025). SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution. arXiv:2507.23348
- [7]Rothfarb et al. (2025). Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Functional Materials Discovery. arXiv:2512.13930
Author
Evgenii Arsentev
PhD · Chief Executive Officer, digital health
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