Existing textual-gradient prompt optimisers rely on a single Large Language Model (LLM) to diagnose failures, propose edits, and judge the results, conflating critique and refinement into one model’s judgement. We introduce DialPO (Dialectical Prompt Optimisation), a multi-agent framework that replaces the single- model feedback loop with a structured de- bate: an Advocate defends what the prompt does well, a Critic attacks failures, cross- examination filters their arguments adversar- ially, a data-isolated Synthesis model drafts the revised prompt from surviving arguments alone, and a regression gate rejects edits that degrade batch accuracy. We provide a theo- retical framework identifying three precondi- tions under which multi-agent debate improves the feedback-reasoning bottleneck of textual- gradient methods, and show that DialPO in- stantiates them by construction. Across 12 benchmarks spanning classification, reasoning, summarisation, and question answering, Di- alPO achieves the highest scores across various Target models, winning or tying a majority of datasets in head-to-head comparisons against each baseline while using 8–18× fewer LLM calls than beam-search optimisers. Compo- nent ablations across five Target models reveal that DialPO’s advantage scales inversely with Target-model strength, clarifying when dialec- tical debate helps prompt optimisation.