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To run a multi-agent debate, prompt the system with your topic to spawn specialized agents that respond in round-robin fashion with full shared context, culminating in a structured synthesis of consensus, disagreements, and action steps. This process reduces blind spots and surfaces stronger decisions.

Multi-agent orchestration for decision-making is a process that spawns several specialized AI agents to analyze a topic in parallel. It manages a round-robin conversation, preserves transcripts, and merges agreements and disagreements into a structured final recommendation.

You can pressure-test a plan by initiating a structured debate among multiple AI agents, applying interactive steering to inject guidance between rounds. The agents evaluate tradeoffs in parallel and produce a synthesized recommendation of action steps.

Yes, you need the Anthropic and python-dotenv dependencies to orchestrate AI debates. Anthropic provides the underlying model capabilities for the agents, while python-dotenv manages environment configuration required to execute the parallel agent orchestration.

Yes, you can inject guidance into an AI debate between rounds through interactive steering. This feature lets you redirect the discussion when the parallel perspectives need adjustment before the final synthesis of consensus and action steps is generated.

The best way to synthesize disagreements from multiple AI agents is using a structured multi-agent debate. It preserves shared context transcripts during round-robin execution and merges surprising insights, disagreements, and consensus into a clear final recommendation.