Start by clarifying requirements and constraints, then walk through the system architecture from data ingestion to response delivery, explicitly addressing reliability, security, observability, and human review. Emphasize trade-offs and justify your choices, showing how you would handle browser automation fragility and integrate with the user API.
Pro tip: Proactively discuss fallback mechanisms for browser automation failures, such as retries with exponential backoff and circuit breakers, and highlight the importance of idempotency to avoid duplicate responses.
Ask questions to understand ticket volume, latency requirements, security policies, and existing infrastructure. Confirm that browser automation is the only option and identify any rate limits or compliance needs.
Outline components: a browser automation service to fetch tickets, a user context service to call the user API, an AI response generator, and a human review queue. Describe data flow and integration points.
Discuss how to make browser automation robust (headless browsers, retries, error handling) and scale horizontally. Mention queueing, load balancing, and caching user context to reduce API calls.
Explain authentication/authorization for browser automation and user API, data encryption, and audit logs. Describe monitoring, logging, and tracing to track system health and AI response quality.
Propose a workflow where low-confidence AI responses are flagged for human review, and feedback is used to improve the model. Discuss metrics like response accuracy and review rate.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.