{"id":18053,"date":"2026-06-10T08:00:43","date_gmt":"2026-06-10T08:00:43","guid":{"rendered":"https:\/\/megagon.ai\/?post_type=faq&p=18053"},"modified":"2026-06-10T08:02:00","modified_gmt":"2026-06-10T08:02:00","slug":"ai-research-paper-data-representation-compound-ai-pipelines","status":"publish","type":"faq","link":"https:\/\/megagon.ai\/jp\/faq\/ai-research-paper-data-representation-compound-ai-pipelines\/","title":{"rendered":"Why do data representation choices matter for multi-agent AI systems?"},"content":{"rendered":"\n

Multi-agent and compound AI systems pass structured outputs between components: JSON schemas, retrieval results, SQL responses, and intermediate state. RePairTQA shows that changing only the format of this data, without changing the content, materially shifts downstream performance. Format design is not a preprocessing detail. It is a system-level decision that affects robustness, coordination, and failure modes across the pipeline.<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Multi-agent and compound AI systems pass structured outputs between components: JSON schemas, retrieval results, SQL responses, and intermediate state. RePairTQA shows that changing only the format of this data, without changing the content, materially shifts downstream performance. Format design is not a preprocessing detail. It is a system-level decision that affects robustness, coordination, and failure […]<\/p>\n","protected":false},"author":4,"template":"","meta":{"footnotes":""},"question-topic":[],"class_list":["post-18053","faq","type-faq","status-publish","hentry"],"yoast_head":"\nWhy do data representation choices matter for multi-agent AI systems? - Megagon<\/title>\n<meta name=\"description\" content=\"Data representation choices shift performance across compound AI pipelines. 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