VI · Multi-AgentEmerging★

Voting-Based Cooperation

also known as Multi-Agent Voting, Agent Consensus by Vote, Inter-Agent Election

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.

Context

A team is running a multi-agent system in which several agents — possibly using different models, different prompts, or different perspectives — produce candidate answers or evaluations on the same task. The system needs to return a single decision, but the agents do not necessarily agree, and the team wants the combined answer to reflect the group rather than whoever happens to speak first.

Problem

Picking any one agent's output as the final answer throws away the diversity of the rest, which was the whole reason for running several agents in the first place. Running an unstructured debate between the agents may not converge within a reasonable budget and offers no clean record of how the final decision was reached. The team needs an explicit procedure that aggregates the agents' opinions fairly, terminates predictably, and leaves an auditable trace showing which agent voted for which option.

Forces

  • Diversity: agents may disagree on a plan or solution; that diversity is the value.
  • Fairness: the procedure must respect each participating agent's standing.
  • Accountability: a vote leaves a traceable record of who chose what.
  • Centralisation risk: voting can entrench whichever agents dominate the electorate.

Example

A medical-triage system runs three specialist agents (cardiology, neurology, pulmonology) over the same patient summary. Each emits a recommended next test. A coordinator presents the three options to five voter agents (general internists) who rank them; the winning option is returned to the clinician, with the full ballot saved for audit.

Diagram

Solution

Therefore:

A coordinator agent collects candidate answers (or reflective suggestions) from a set of worker agents, presents them as a ballot to additional voter agents, and tallies the votes — by majority count, average score, weighted by role, or via a smart-contract / blockchain mechanism for tamper-evidence. Identity management of voters is significant for auditability. Voting-based cooperation can be combined with role-based or debate-based cooperation as a closing step.

What this pattern forbids. No single agent's output may be returned as final; only the option that wins the tally is the agreed decision.

The smaller patterns that complete this one —

  • generalisesSelf-Consistency★★— Sample the same question multiple times at non-zero temperature and aggregate by majority or judge to mitigate hallucination.
  • usesTool/Agent Registry★— Maintain a single queryable catalogue of both available tools and available agents, with metadata (capability, cost, latency, quality) the agent can use to pick the right one for a task.
  • generalisesHeterogeneous-Model Council with Synthesis Judge★— Three or more role-specialized personas run on different model architectures in parallel; a synthesis judge — given only their structured JSON, not the original input — produces the final verdict.

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

  • alternative-toDebate·— Have multiple agents argue different positions on a question and converge through structured exchange.
  • composes-withRole Assignment★★— Assign each agent a named role (researcher, writer, critic, planner) with a role-specific prompt, tool palette, and acceptance criteria.
  • alternative-toBest-of-N Sampling★— Sample N candidate outputs and select the highest-ranked by a reward model or scorer.
  • complementsEvaluator-Optimizer★★— One LLM generates; another evaluates and feeds back; loop until criteria are met.
  • alternative-toParallel Fan-Out / Gather★— Multiple independent agents execute in parallel on a partitioned task; a dedicated aggregator agent reconciles their results into a single output.

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References

Provenance