A single AI's answer often carries bias and blind spots. A more reliable approach: have multiple Agents play opposing roles, using debate to approach the truth. This is the core idea behind multi-agent debate systems.
Using market analysis as an example, design a debate committee:
Round 1: Opening Statements. Each Agent independently outputs its core arguments without referencing others.
Round 2: Cross-Examination. Bulls respond to Bears' arguments, Bears counter Bulls' evidence. Each side sees the full output from the previous round.
Round 3: Closing Statements. Both sides deliver final summaries. The Judge aggregates all arguments for a verdict.
Debates need a factual foundation. Knowledge is organized by recency into layers:
A single model easily falls into "self-persuasion" — once it forms a judgment, subsequent reasoning selectively seeks supporting evidence. Multi-agent debate forcibly introduces adversarial perspectives, where every argument must withstand counter-arguments. This resembles human "red teaming" or academic peer review.
This architecture applies beyond investment analysis — it's equally useful for policy evaluation, technical proposal review, legal reasoning, and any decision scenario requiring multi-perspective weighing.