VI · Multi-AgentExperimental·

Debate

also known as Multi-Agent Debate, Adversarial Debate

Have multiple agents argue different positions on a question and converge through structured exchange.

Context

A team is using agents on questions whose answers are genuinely contested or where the user explicitly wants to see the strongest case both for and against — should this firm adopt a particular open-source library, is this regulatory interpretation defensible, does this design choice hold up under scrutiny. The cost of a confidently wrong single answer is high enough to justify spending extra model calls.

Problem

A single agent answering directly tends to hide its own reasoning blind spots: whatever case it considered first becomes the answer, and the counter-arguments never get articulated. Asking the same model to critique its own answer reinforces the original framing rather than challenging it, because both passes share the same priors. Without an explicit opposing voice, the team gets a confident answer with no view of what it might be missing.

Forces

  • Genuinely independent positions are hard to engineer with one model.
  • Debate length must be bounded.
  • A judge is needed to decide; the judge has its own biases.

Example

A policy-analysis agent answers 'should the firm adopt this open-source library?' with a confident yes that turns out to ignore a license incompatibility. Single-shot answers hide the reasoning the model didn't do. The team uses Debate: two agents argue opposing positions — one for adoption, one against — exchanging structured arguments for a fixed number of rounds, and a third agent reads the transcript and rules. The license question surfaces in the second round and changes the verdict.

Diagram

Solution

Therefore:

Two or more agents are given different positions. They exchange arguments over N rounds. A judge agent (or a tie-break rule) selects the answer or synthesises a position from both.

What this pattern forbids. Each debater may only argue its assigned position until the judge step.

The smaller patterns that complete this one —

  • generalisesSwarm·— Run many peer agents that interact directly without a central supervisor, achieving emergent coordination.

And the patterns that stand alongside it, or against it —

  • alternative-toInner Committee★— Run one model under several distinct personas (executor, critic, planner) within a single agent loop.
  • complementsSelf-Consistency★★— Sample the same question multiple times at non-zero temperature and aggregate by majority or judge to mitigate hallucination.
  • alternative-toInfinite Debate✕— Anti-pattern: launch multi-agent debate without a termination rule and watch the agents loop forever.
  • alternative-toCommunicative Dehallucination★— When an instructed agent would have to invent missing context to comply, have it reverse roles and ask the instructor for the missing detail before answering.
  • alternative-toVoting-Based Cooperation★— Finalise a decision across multiple agents by collecting and tallying their votes on candidate options, so the joint output reflects collective rather than single-agent judgement.
  • alternative-toParallel-Voice Proposer·— Generate several candidate thoughts in parallel under named voices and have the same model pick the canonical one, logging the losers as audit.

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References

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