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Reasoning

Multi-Agent Debate for Better LLM Reasoning

A multi-agent debate LLM setup runs the same question through three roles — one model proposes an answer, another attacks it, and a judge decides who was right — instead of trusting a single pass. The appeal is obvious: adversarial scrutiny catches mistakes that a lone model, confidently wrong, will happily ship. The catch is just as real: you pay for it in latency and tokens. By the end of this post you'll know exactly what the Proposer → Critic → Judge graph does in Promptise Foundry, how to stand one up in a few lines, and — just as importantly — the narrow set of queries where it earns its keep versus the far larger set where a single verify pass is the smarter buy.

Reflection, Self-Critique & Self-Consistency Agents

A self-critique agent is an LLM that checks its own work before it answers — it drafts a response, inspects that draft for mistakes, and fixes them in the same step. The appeal is obvious: catch the careless error before the user ever sees it. The catch is that reflection is not free, and most teams bolt it onto models that don't need it. By the end of this article you'll know which introspection techniques pay for themselves, which mostly burn tokens, and how to add a cheap self-check to a small model in a single line.