Product Hive

Turn an idea into a product plan — AI agents debate, refine, and build your PRD and Scrum board.

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About ProductHive

Inspiration

Building a great product always takes a team—product managers, designers, engineers, data, and business minds all debate, push, and refine ideas. But what if anyone, anywhere, could access that level of structured, expert-driven debate and decision making? We were inspired to create ProductHive to democratize the experience of a high-performing product team. Our goal: let a single prompt trigger a rich, transparent, multi-perspective debate driven by AI agents, resulting in a high-quality Product Requirements Document (PRD) and delivery plan.

What it does

ProductHive is an AI-powered multi-agent debate system that transforms a simple user prompt into a fully debated, reasoned, and actionable PRD. Each agent represents a key stakeholder (UX, Backend, Frontend, Database, Business) and debates the requirements from their perspective. Users can interact with the debate, follow the discussion, and see every decision and tradeoff. When consensus is reached, a complete PRD is generated for export or further use.

How we built it

  • Multi-Agent System: Leveraged Google Vertex AI’s Agent Development Kit (ADK) to build specialist agents (UX, Backend, DB, Frontend, Business) and a coordinator architect agent.
  • Debate Orchestration: Wrote Python orchestration logic to run debate rounds, track history, and detect consensus.
  • PRD Extraction: Developed robust tools to parse natural language agent arguments into structured PRD sections.
  • Persistence: Used Google Firestore to save and load debate sessions, ensuring the auditability of every decision.
  • User Interface: Built a responsive JavaScript frontend for users to initiate prompts, join debates, and manage PRDs.
  • Export: Enabled PRD export to standard formats, ready for use in project management or documentation workflows.

Challenges we ran into

  • Coordinating Multi-Agent Debate: Ensuring agents could debate, disagree, and reach consensus in a way that felt “real” and productive was complex.
  • Extracting Structure from LLM Output: Designing logic to reliably translate varied agent responses into a coherent, structured PRD was an iterative process.
  • Balancing Transparency & Usability: Making the debate process visible and understandable to users, without overwhelming them with information.
  • Extensibility: Architecting the system so new agents, debate rules, or PRD templates could be added with minimal friction.

Accomplishments that we're proud of

  • Created an end-to-end workflow where anyone can get a high-quality PRD from a simple prompt, with every decision traceable and auditable.
  • Delivered a robust, extensible system that models real-world product team dynamics.
  • Built a tool that is both technically sophisticated and approachable for non-technical users.

What we learned

  • AI as Team: Multi-agent AI “teams” can reason more robustly and transparently than solo models in complex domains.
  • Structure Matters: Aligning AI output to industry-standard templates (like PRDs) makes the results dramatically more useful.
  • User Trust: Showing the full debate and decision history helps users trust and adopt AI-generated outcomes.

What's next for ProductHive

  • More Agent Specialties: Add agents for data science, security, legal, and more to broaden debate perspectives.
  • Integration with Project Management Tools: Directly push finalized PRDs into platforms like Jira, Asana, or Notion.
  • Voice and Language Support: Enable voice-driven debates and multilingual agents.
  • Smarter Consensus Models: Experiment with more sophisticated decision-making and objection-handling among agents.
  • Community Templates & Plugins: Let users contribute their own agent roles, debate rules, and PRD templates to make ProductHive even more powerful.

ProductHive is just getting started. We can’t wait to see how teams and solo makers use AI-powered debate to build better products, faster.

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