You are helping me build the following AI agent workflow. ## Goal Use Case Governance Classifier: Submits an AI use case via webhook to screen for risk, classify it as normal, elevated, or prohibited, generate a checklist. ## Specification - What it does: You submit AI use cases via webhook to instantly get screened for risk and classified as normal, elevated, or prohibited with a generated checklist. The system automatically logs your data in Supabase and sends email notifications so you always have a clear audit trail. - Trigger: Triggered by an event (Event ยท on webhook submission) - Autonomy: Fully hands-off - Expected setup effort: about an afternoon - Tools/services involved: - Anthropic: AI risk screening and classification - Supabase: Logging AI use case records - Send Email: Sending notifications to stakeholders ## Known pitfalls, handle each one explicitly in your implementation 1. Supabase row-level security policies might block writes if not configured correctly so verify the service role key permissions before deployment. 2. Email notifications may fail silently if the recipient address is invalid so implement a retry mechanism with error logging. 3. LLM outputs can vary in format which breaks downstream parsing so enforce a strict JSON schema response structure. ## Reference implementation https://n8n.io/workflows/15879 (workflow template) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/ai-use-case-governance-classifier/ via usecasesforagents.com