debate

Structured arguments for decision support are orchestrated by configuring proposer and challenger tools across multi-round debates. This process requires explicit evidence for all claims and produces a final verdict with traceable sources for reproducible evaluation.

Evidence-based multi-round debate is a mechanism where AI tools argue proposer and challenger roles across configurable rounds. It enforces evidence-backed claims, tracks context and concessions, and generates a summarized verdict with round-by-round reasoning.

You can configure different AI tools for proposer and challenger roles. The debate execution supports configurable models, allowing you to specify distinct tools for each role alongside adjustable rounds and evaluation rules.

To get a summarized verdict with traceable sources from AI argumentation, execute the configured multi-round debate. The process maintains round-by-round context, tracks concessions, and synthesizes the final arguments into a verdict with explicit evidence.

The best way to check bias in AI-generated conclusions is running a structured multi-round debate. By assigning proposer and challenger roles to different tools, the process surfaces evidence-backed arguments and performs bias checks through rigorous argumentation.

You should not use multi-round AI debates for hypothesis testing when your topic lacks sufficient evidence to support claims. The process strictly enforces explicit evidence for all arguments, making it unsuitable for purely subjective or unsupported assertions.