I came in with a plan: triage agent feeds into an investigate agent feeds into a resolve agent, clean pipeline.
Start by clarifying the scope and requirements of the agent system, such as ticket types, resolution actions, and success metrics. Then outline a high-level architecture with components like ticket ingestion, classification, investigation, resolution, and feedback loop. Finally, dive into trade-offs around automation vs. human-in-the-loop, scalability, and reliability.
Pro tip: Emphasize the importance of a human-in-the-loop for high-risk actions and continuous learning from resolved tickets to improve the agent over time. This shows you balance automation with safety and adaptability.
Ask questions to understand the types of support tickets, expected resolution actions, SLAs, and constraints like data privacy. Define what 'resolve' means (e.g., auto-reply, issue refund, escalate).
Propose a modular system: ingestion (from ticketing systems), classification (NLP models), investigation (querying knowledge bases, logs, APIs), resolution (executing actions), and feedback (learning from outcomes).
Detail key components: how to classify tickets (e.g., intent recognition), how to investigate (e.g., retrieval-augmented generation, tool use), and how to execute resolutions safely (e.g., sandboxing, approvals).
Address trade-offs: automation vs. human oversight, latency vs. accuracy, cost vs. performance. Discuss scaling to millions of tickets, handling failures, and monitoring.
Define metrics (resolution rate, customer satisfaction, cost per ticket) and a feedback loop to improve the agent. Mention A/B testing and gradual rollout.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.