Start by clarifying requirements and constraints, then propose a modular agentic architecture with clear separation of concerns: ingestion, classification, evidence gathering, diagnosis, recommendation, and safe execution. Emphasize human-in-the-loop for risky actions and discuss trade-offs around automation, latency, and accuracy.
Pro tip: Anchor your design around a confidence score and risk assessment for each action, and explicitly define the thresholds and fallback mechanisms for human intervention. This shows you prioritize safety and reliability over full automation.
Ask about ticket volume, types, SLAs, existing tools, and what 'safe remediation' means. Establish non-functional requirements like accuracy, latency, and auditability.
Outline stages: ticket ingestion, classification (using ML/NLP), evidence gathering (logs, metrics, code), root cause analysis (causal inference, knowledge base), resolution recommendation, and optional execution.
Specify when humans are involved: for low-confidence diagnoses, high-risk actions, or novel issues. Include approval workflows, rollback plans, and audit trails.
Discuss how the system handles high ticket volume, ensures low latency, and maintains accuracy. Mention monitoring, feedback loops, and continuous learning.
Compare full automation vs. human-in-the-loop, rule-based vs. ML approaches, and centralized vs. distributed agents. Justify your choices based on requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.