When network downtime costs customers $5,000+ per minute, agents can't afford to spend 20 minutes searching for answers.
AI-assisted triage correlates error logs with known issues, auto-populates diagnostic context, and routes cases to specialists with recommended solutions—cutting resolution time by surfacing answers agents would otherwise spend 20+ minutes searching for across manuals and ticketing systems.
Agents toggle between 8–12 systems to find firmware bulletins, SNMP trap definitions, and RMA procedures. Each search burns minutes while customers wait on hold.
Syslog errors and SNMP traps require specialized knowledge to decode. Junior agents escalate to senior engineers, adding queue time and delaying resolution.
Different agents classify the same firewall error differently, routing cases to wrong teams. Tickets bounce between hardware, firmware, and configuration groups.
The platform parses syslog entries, SNMP traps, and device telemetry the moment a case opens. It matches error signatures against 500,000+ historical cases, flags known CVEs, and checks firmware version compatibility. Agents see a pre-populated diagnostic summary with recommended next steps—no manual log decoding required.
Automated triage routes cases based on failure type, device model, and SLA tier. Switch crashes go to hardware specialists with part numbers already attached. Firewall misconfigurations route to network engineers with relevant config snippets highlighted. Agents skip the 10-minute detective phase and jump straight to resolution.
Autonomous agent correlates SNMP traps with known router firmware bugs, routes to hardware team when pattern matches failed line card signatures.
Instantly summarizes 47-email escalation chains about firewall packet drops, highlighting which config changes preceded the outage.
Analyzes switch age, warranty status, and failure type to recommend RMA or field upgrade, reducing unnecessary part swaps by 34%.
Router and switch failures trigger cascading outages affecting hundreds of downstream devices. A misconfigured firewall rule can black-hole traffic for an entire data center. Customers expect sub-hour MTTR because every minute of downtime has a measurable P&L impact.
Network equipment generates dense diagnostic telemetry—syslog streams, SNMP trap floods, CPU/memory graphs, and routing table snapshots. Agents must decode this fire hose while simultaneously checking firmware CVE bulletins, RMA eligibility, and configuration backup histories. Traditional ticketing systems force agents to manually stitch together context from 8+ tools before they can even diagnose root cause.
The system flags unknown error signatures and routes to senior engineers with full diagnostic context. Each resolution trains the model, so novel issues become recognizable patterns for future cases.
Agents can override routing with a single click. The platform learns from overrides—if 3+ agents reroute the same error pattern, the model adjusts classification logic automatically.
Yes. The platform ingests syslog and SNMP data regardless of OEM. It correlates error patterns across Cisco, Juniper, Arista, and custom network OS implementations using normalized failure signatures.
New hires resolve cases at 70–80% of senior agent speed within 2 weeks instead of 8–12 weeks. Pre-populated diagnostics and guided troubleshooting eliminate the steep learning curve for SNMP trap interpretation.
The platform detects correlation drift when resolution patterns shift post-update. You upload new firmware release notes, and the AI retrains classification logic to match updated error semantics within 24 hours.
Understanding and optimizing the issue resolution curve.
Part 1: The transformation of IT support with AI.
Part 2: Implementing AI in IT support.
See how agents resolve network equipment cases 65% faster with AI-assisted triage.
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