openprd-discovery-loop

Automate deep-dive requirements analysis by orchestrating autonomous sub-agents to scan the repository, cross-verify technical claims, and map structured evidence to generate validated specifications.

Multi-agent discovery is the process of orchestrating parallel sub-agents to perform deep research and risk assessment across complex codebases, ensuring high-confidence output through iterative verification loops.

Ensure evidence-based analysis by requiring every technical claim to be backed by source paths and confidence scores, maintaining high documentation standards for product and engineering workflows.

Yes, you can use agentic discovery to scan legacy modules, compare them against reference projects, and generate a validated requirements specification aligned with existing project standards.

Autonomous discovery works for comparing complex repositories by running parallel sub-agents that perform multi-perspective analysis and cross-verification of technical claims against industry-standard reference projects.

Agentic discovery loops require structured evidence mapping and iterative verification to ensure high-confidence output, meaning manual overhead increases if repository structure is highly ambiguous or undocumented.