Agents address the problem from independent angles, other agents try to refute what they found, and the run keeps iterating until the answers converge—which is how a workflow reaches results a single pass can't.
Convergence through adversarial iteration is borrowed from ensemble methods and scientific peer review — but applied to code. The non-obvious implication: this architecture is more robust to the hallucination problem than single-pass generation, because refuting agents are specifically incentivized to find failures. It's a form of AI quality control built into the workflow itself.