Design an Automated Ticket Investigation Agent

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Quick Overview

This question evaluates a candidate's ability to design AI-enabled agentic systems for automated ticket investigation, covering competencies in ticket understanding, classification and prioritization, multi-source evidence retrieval (logs, metrics, traces, documentation), root-cause diagnosis, remediation orchestration, and human-in-the-loop safety controls. It is commonly asked in the ML system design domain to assess architectural and integration thinking, data retrieval and evaluation strategies, production monitoring and safety considerations, and tests both high-level conceptual architecture and practical application-level implementation concerns.

Design an Automated Ticket Investigation Agent

Company: Meta

Role: Software Engineer

Category: ML System Design

Difficulty: hard

Interview Round: Technical Screen

Design an AI-enabled agentic system that automatically investigates support or engineering tickets. The system should: - Read an incoming ticket and understand the user's issue. - Classify and prioritize the ticket. - Gather evidence from internal tools such as logs, metrics, traces, documentation, runbooks, deployment history, and previous incidents. - Produce a diagnosis or likely root cause. - Recommend a resolution, escalation path, or next action. - Optionally execute safe remediation steps when allowed. - Keep humans in the loop for risky actions. Discuss the agent loop, architecture, tool integrations, data retrieval, safety controls, evaluation strategy, and production monitoring.

Overview: This question evaluates a candidate's ability to design AI-enabled agentic systems for automated ticket investigation, covering competencies in ticket understanding, classification and prioritization, multi-source evidence retrieval (logs, metrics, traces, documentation), root-cause diagnosis, remediation orchestration, and human-in-the-loop safety controls. It is commonly asked in the ML system design domain to assess architectural and integration thinking, data retrieval and evaluation strategies, production monitoring and safety considerations, and tests both high-level conceptual architecture and practical application-level implementation concerns.

Read the full Meta Software Engineer interview experience this question came from

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Apr 27, 2026
hardSoftware EngineerTechnical ScreenML System Design
11
0

Design an AI-enabled agentic system that automatically investigates support or engineering tickets.

The system should:

  • Read an incoming ticket and understand the user's issue.
  • Classify and prioritize the ticket.
  • Gather evidence from internal tools such as logs, metrics, traces, documentation, runbooks, deployment history, and previous incidents.
  • Produce a diagnosis or likely root cause.
  • Recommend a resolution, escalation path, or next action.
  • Optionally execute safe remediation steps when allowed.
  • Keep humans in the loop for risky actions.

Discuss the agent loop, architecture, tool integrations, data retrieval, safety controls, evaluation strategy, and production monitoring.

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