PRDs, prototypes and AI agents

Product teams are arguing over whether the best spec for an AI feature is still a document — or a working prototype the team can test immediately. (developers.openai.com) The debate picked up in April 2026 as product leaders traded examples on X of “AI-ready” product requirements documents, shorter prototype-first workflows, and agent tools that turn requirements into deliverables. StoriesOnBoard posted on July 19, 2025 that teams can use artificial intelligence to convert a product requirements document into a story map, a planning view that breaks work into user activities and tasks. (x.com) (storiesonboard.com) On July 20, 2025, product executive Aakash Gupta posted that some teams are moving from long docs toward prototypes, especially when artificial intelligence can generate interfaces and flows quickly enough to replace part of the old handoff from product manager to designer. Another X post the same day listed core sections for an AI-focused product requirements document, including user flows, edge cases, and success metrics. (x.com 1) (x.com 2) A product requirements document, or PRD, is the file that states what a team is building, who it is for, and how success will be measured. OpenAI says agents are software systems that plan, call tools, and keep enough state to complete multi-step work, which means vague instructions can now be executed at scale instead of just debated in meetings. (beam.ai) (developers.openai.com) That has changed what teams put in the spec. OpenAI’s guide for agent evaluations says teams should test workflows with datasets, traces, graders, and evaluation runs, while Anthropic says companies should define specific, measurable success criteria before deployment. (developers.openai.com) (assets.anthropic.com) In practice, that pushes PRDs away from broad language like “fast” or “handle errors gracefully” and toward explicit requirements such as latency targets, failure states, tool permissions, and approval steps. Microsoft’s Copilot Studio documentation says agent flows are deterministic, meaning the same input follows the same rule-based path, and its evaluation tools generate test cases to measure answer quality against business requirements. (learn.microsoft.com 1) (learn.microsoft.com 2) (learn.microsoft.com 3) Prototype-first advocates are making a narrower argument than “docs are dead.” They are saying that for interface-heavy features, a clickable mockup or generated front end can expose missing states, broken navigation, and weak prompts faster than a six-page brief. (openai.com) (moonchild.ai) Document-first advocates are making a different point: an agent cannot reliably build or test what the team has not specified. OpenAI’s latest Agents software development kit update added sandbox execution and a model-native harness for work across files and tools, which raises the cost of ambiguity because the system can now take more actions on its own. (openai.com 1) (openai.com 2) That is why the middle ground is gaining traction: write the parts a machine and a team both need, then use prototypes and agents to pressure-test the gaps. In that workflow, the PRD becomes a structured input for story maps, test sets, and prototypes instead of the final artifact everyone waits on. (x.com) (developers.openai.com) (learn.microsoft.com) The argument is no longer about whether to keep writing PRDs. It is about whether the first useful version of a requirement is a paragraph, a prototype, or an agent run that shows exactly where the spec fails. (x.com) (x.com)