> ## Documentation Index > Fetch the complete documentation index at: https://docs-sre.lightrun.com/llms.txt > Use this file to discover all available pages before exploring further. # Support Engineers > AI SRE workflow for Support Engineers Support Engineers are often the first to interact with incidents from customer reports or monitoring systems. AI SRE helps correlate customer reports with system alerts and provides technical context for effective responses. ## Workflow stages ### Alert intake & triage **Challenge:** Receives customer reports with vague symptoms; struggles to correlate tickets to alerts **How AI SRE helps:** * Correlates customer symptoms with system issues * Maps customer tickets to relevant alerts * Provides technical context from customer descriptions * Assesses severity based on customer impact **Example:** ``` Customer: "Checkout is slow" You: "Why is checkout slow for customers?" AI SRE: [Correlates with alerts, identifies performance issues] ``` ### Scope & impact assessment **Challenge:** Escalates based on customer complaints rather than system insight **How AI SRE helps:** * Provides evidence-based impact assessment * Identifies affected services and dependencies * Quantifies impact beyond customer complaints * Provides technical evidence for escalation **Example:** ``` You: "What's the impact of checkout being slow?" AI SRE: [System-level impact: services, user %, business impact] ``` ### Root cause investigation **Challenge:** Relays findings manually between teams **How AI SRE helps:** * Performs investigation automatically * Gathers evidence from multiple sources * Provides shareable investigation results * Findings can be shared directly with engineering teams ### Fix design **Challenge:** Communicates assumptions back to customers **How AI SRE helps:** * Provides evidence-based explanations * Translates technical findings clearly * Indicates confidence levels * Enables accurate status updates ### Deployment & verification **Challenge:** Waits for confirmation to update customers **How AI SRE helps:** * Provides real-time system status * Verifies when fixes are deployed and working * Monitors impact reduction * Enables timely customer communication ### Post-incident learning **Challenge:** Writes support summaries with limited technical depth **How AI SRE helps:** * Provides technical details for summaries * Documents investigation timeline * Includes evidence chain * Creates comprehensive summaries ## Key workflows ### Customer report correlation 1. Receive customer report with vague symptoms 2. Ask AI SRE to investigate based on customer description 3. Get technical correlation with system alerts 4. Map customer issue to specific system problems 5. Escalate with technical context ### Impact assessment 1. Get customer complaint 2. Ask AI SRE for system-level impact assessment 3. Understand blast radius and severity 4. Make informed escalation decision 5. Communicate impact to stakeholders ## Best practices * Use AI SRE immediately when reports come in * Correlate customer reports with alerts systematically * Get technical context before escalating * Provide technical evidence when escalating * Use AI SRE findings for comprehensive documentation ## Next steps