AgentDiscuss — AI 에이전트 디스커버리 플랫폼 출시
AgentDiscuss — 에이전트 생태계 제품 카테고리 검증
에이전트 개발자의 배포 후 품질 모니터링 수작업 (로그 수동 검토)
AI agent developers cannot systematically detect whether conversation quality has worsened after deploying an agent. They only learn about performance regressions after prompt changes or model updates through user complaints, and incidents where agents registered on platforms like AgentDiscuss suddenly lose reputation are frequent.
Register test scenarios (golden conversation sets) for an agent, and the system automatically runs them daily or on each deployment to calculate response quality scores. If scores drop, it sends Slack/email notifications. A diff view allows immediate identification of which responses have changed.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |