Automated Closed-Loop from Alert to Remediation
Operations teams don't face single-system problems but cross-system chain efficiency issues — checking alerts in monitoring, pulling logs from servers, recording conclusions in ticketing, reporting results in group chats. FIM Agent transforms this chain from manual handoffs to automated execution.
Alert volume far exceeds human processing capacity
Production monitoring systems generate hundreds to thousands of alerts daily. Many are duplicates, correlated alerts, or low-priority noise. Operations staff spend significant time deciding 'should this alert be handled?' while truly urgent P0 incidents get buried in the list.
A single investigation involves manual operations across four to five systems
After discovering an alert: first check details in Prometheus or Zabbix; then SSH into the server for application and system logs; if recent deployments are involved, check CI/CD release records. After investigation, record conclusions in Jira or internal ticketing, then report in Feishu group. Every step requires manual context switching.
Investigation quality depends on individual experience
Senior engineers' investigation approaches — which logs to check first, which metrics matter, which symptoms indicate which root causes — stay in their heads. New team members facing the same alert start from scratch, taking 3-5x longer. Experience can't be distilled into reusable standard procedures.
Alert Reception & Preprocessing
Monitoring systems push alerts to FIM Agent via Webhook. Agent auto-performs deduplication (merging repeated alerts from the same source), correlation analysis (identifying multiple alerts from the same fault), and priority assignment (classifying as P0-P3 based on alert type and impact scope).
Auto Log & Context Collection
Agent collects relevant information through connectors or built-in tools: application and system logs (via Shell tools or log platform APIs), recent deployment records and config changes (via CI/CD connectors), related service performance metrics (via monitoring system APIs). Multiple collection tasks run in parallel.
Root Cause Analysis
Agent submits collected logs, metrics, and change records to LLM for analysis. Simultaneously searches the knowledge base for historical fault cases (past handling records for similar alerts). Generates a root cause diagnosis report: lists possible causes with confidence levels, correlates historical similar cases, recommends remediation actions.
Push & Confirmation
Diagnosis report pushed to on-call staff via Feishu interactive cards. Cards include: alert summary, root cause analysis, recommended action buttons. On-call staff select actions directly on the card, and Agent auto-executes after confirmation.
Execution & Recording
Agent executes remediation actions and monitors results. Auto-updates ticketing system: records alert details, diagnosis process, remediation actions, and outcomes. Closes alert and notifies relevant teams. Full operation chain is retraceable.
Alert handling shifts from 'people chasing systems' to 'systems finding people'
Agent completes preprocessing and initial diagnosis, then pushes only key decisions to on-call staff. People no longer passively bounce between systems, but make judgments when pushes arrive.
Investigation expertise transforms from personal memory to organizational asset
Every alert's diagnosis process and remediation results are auto-deposited into the knowledge base. When new alerts occur, Agent auto-searches similar historical cases. Senior staff's experience is transmitted to the entire team through Agent.
Full remediation process is auditable
Complete operation chain recorded from alert trigger to final closure. Supports SLA statistics and fault post-mortem analysis.