sadd:do-competitively

Multi-agent competitive synthesis improves task quality by running parallel agents to generate diverse solutions, evaluating them with multiple judges, and combining the strongest elements into a final evidence-based result.

You orchestrate multiple AI agents by providing a task description and constraints, allowing the system to run three independent solutions in parallel, evaluate them, and produce a polished final output.

Yes, multi-judge evaluation verifies complex AI outputs by using structured assessment loops to ensure correctness, while self-critique mechanisms refine ideas before final synthesis.

Automated orchestration balances cost and quality through adaptive strategy selection, dynamically choosing to polish, redesign, or fully synthesize outputs based on the specific task requirements.

You should avoid multi-agent synthesis for speed-critical tasks, as this orchestration approach is specifically designed for high-stakes scenarios where quality matters significantly more than rapid execution.