Self-Healing Network Monitor – Ansible, Playwright & AI Analytics
87%
Of incidents auto-remediated
Engineered an autonomous network monitoring platform combining Ansible remediation playbooks, Playwright-driven synthetic testing, and an AI layer that provides real-time health analysis — diagnosing faults and triggering background remediation before users ever notice an issue.
Challenges
- Network incidents were reactive — engineers only discovered issues after users reported them, often 20–40 minutes into an outage.
- Repetitive remediation tasks (interface resets, BGP session restarts, DHCP lease flushes) consumed hours of engineer time weekly on toil that could be automated.
- No single pane of glass for network health — data was fragmented across vendor dashboards with no correlated intelligence.
Solutions
- Built a real-time network analytics dashboard aggregating SNMP, syslog, and flow data into a unified interface with live topology visualization.
- Integrated an AI layer that continuously analyzes health signals — flagging anomalies, predicting degradation patterns, and surfacing a plain-English status summary of network condition.
- Deployed Ansible remediation playbooks covering the most common fault classes — interface bounces, routing protocol resets, DNS/DHCP recovery, and switch config drift correction.
- Used Playwright for synthetic end-to-end connectivity probes running on a scheduled loop, validating critical paths and triggering playbooks the moment a probe fails.
- Built an alerting pipeline that pages engineers only when automation fails to resolve an issue, eliminating noise from self-correcting events.
Outcomes
- 87% of detected network incidents resolved autonomously before user impact — no ticket opened, no engineer paged.
- Mean time to remediation dropped from an average of 34 minutes to under 4 minutes for automated fault classes.
- Engineer toil from repetitive remediation tasks reduced by over 70%, freeing the team for higher-value infrastructure work.
- AI health summaries provided instant situational awareness, reducing time spent correlating logs during incidents by 65%.