Teams building assistant and agent experiences—product managers, ML engineers, and developer teams—are drowning in conversational telemetry and noisy user feedback that is hard to attribute to a specific agent, prompt, or UI context. With an estimated 800,000 developer/product teams and a $3.2B market ($4K ACV per team), the core pain is not volume but signal-to-noise: teams spend engineering cycles chasing anecdotal reports instead of reproducible, contextual issues tied to agent version, prompt template, or UX state. A practical product would ingest conversation logs and feedback from multiple sources, enrich them with agent, model-version, prompt and UI-state metadata, and expose fast semantic search + filters by agent, intent, and session context using embeddings/vector search. The UI should surface prioritized, reproducible issues (heatmaps of failure modes, timeline traces, root-cause links to prompts or code), plus one-click exports to issue trackers and privacy controls for PII, targeting a developer-centric workflow that justifies the ~$4K ACV for teams that ship agents to users. This is an attractive moment because agent proliferation, cheap semantic search, and a shift toward developer-owned observability create clear technical enablers and buyer appetite; the market and revenue potential scores of 92/100 and 88/100 reflect that. Competition is medium—observability vendors and generic feedback tools exist—but few combine agent-aware metadata, semantic search, low-friction SDKs, and privacy-first ingestion; the main challenges will be integrating diverse agent frameworks at scale, proving ROI quickly to justify purchase, and staying ahead of larger observability players who could add similar features.