feature-discovery

Scan AI market signals for evidence-based product upgrades by aggregating credible developments within a seven-day window, evaluating them for durability and governance, and outputting five vetted recommendations with source citations to product/ideas.md.

Evidence-based strategic product discovery for enterprise AI agents identifies high-impact opportunities by aggregating credible market signals within a tight seven-day window, ensuring outputs satisfy provenance and fail-closed execution requirements with explicit source citations.

Yes, you can generate source-backed product recommendations for a fail-closed runtime environment by scanning credible AI and agent market signals, prioritizing provenance and governance, and writing exactly five vetted strategic upgrades to product/ideas.md.

Document strategic product opportunities for CI regressions and leadership audits by writing five evidence-backed strategic upgrades with explicit source citations to product/ideas.md, ensuring traceability and audit readiness.

When market evidence is weak, the product recommendation process outputs no recommendations, adhering strictly to evidence-first requirements and ensuring no unsupported strategic upgrades are written to product/ideas.md.

Evidence-based product management works for enterprise-scale agent runtime contexts by applying a seven-day market signal scan to identify strategic upgrades where durable runtime, provenance, and governance matter, outputting five source-cited recommendations.