Where Resolvify goes to work
High-impact use cases for autonomous IT operations
Start where the noise and toil are highest: L1 incidents, cloud ops, network, and change windows. Resolvify brings governed automation to the workloads that burn the most cycles.
Pilot targets vary by environment; typical results are measured over 30–90 days after onboarding and automation coverage rollout.
Resolvify reduces change-induced incidents by enforcing guardrails during maintenance and validating post-change stability automatically.
Use case overview
L1 / L1.5 incident automation
Before → After: Queue backlog and escalations → Auto-triage and resolution before SRE
View details →Cloud operations remediation
Before → After: Manual fixes across regions → Self-healing with guardrails
View details →Network incident automation
Before → After: Interface down, BGP, certs—manual → AI-assisted diagnosis and fix
View details →Change window protection
Before → After: Rollbacks eat the night → Drift caught, rollback in minutes
View details →L1 / L1.5 incident automation
Context / problem
- Queue backlog—tickets pile faster than L1 can triage
- Noisy alerts drowning signal in noise
- Escalations to SRE for repeat issues that should be auto-resolved
How Resolvify helps
- Understand: Correlate ticket + telemetry to classify incident and select remediation path
- Resolve: Execute runbook with approval gates; validate health checks; update ticket automatically
- Improve: Learn success patterns; quarantine failing automations; continuously raise auto-resolution rate
Example runbooks
- Disk 95% → clean temp, expand volume with approval
- Service unresponsive → controlled restart with health check validation
- JVM OOM → capture diagnostics, controlled restart, notify on-call
- Pod crash loop → gather logs, controlled restart with rollout validation
- Password expiry → reset flow with approval workflow
Outcomes
- 40–60% MTTR reduction for L1/L1.5 incidents
- 50–70% of repeat tickets auto-resolved before escalation
- 30–50% fewer escalations to SRE
Guardrails used
RBAC • approvals • change window awareness • rollback • audit trail • quarantine after failures
Cloud operations remediation
Context / problem
- Manual fixes across regions and accounts
- Drift between prod and staging goes undetected
- Scaling and capacity issues require manual intervention
How Resolvify helps
- Understand: Correlate cloud metrics, logs, and config drift to identify root cause
- Resolve: Execute remediation with approval gates; scale, restart, or rollback per policy; update ITSM automatically
- Improve: Learn which remediations work across environments; quarantine patterns that fail
Example runbooks
- EC2/VM high CPU → scale out or controlled restart with approval
- RDS connection exhaustion → terminate idle sessions per policy, alert DBA/on-call
- S3 bucket policy drift → detect and propose fix for approval
- Kubernetes node NotReady → cordon, drain, replace per runbook
- Lambda throttling → increase concurrency per policy, alert
Outcomes
- Self-healing at scale across cloud regions
- Drift detected and corrected in minutes, not days
- Fewer manual runbooks to maintain
Guardrails used
RBAC • approvals • change window awareness • rollback • audit trail • quarantine after failures
Network incident automation
Context / problem
- Interface down, BGP flapping—manual diagnosis and fix
- Certificate expiry caught too late
- DNS/routing issues require tier-2 escalation
How Resolvify helps
- Understand: Correlate network telemetry and ticket context to identify root cause
- Resolve: Execute runbook with approval; validate link state, controlled actions; update ticket automatically
- Improve: Detect recurring patterns; quarantine failing remediations; refine runbooks
Example runbooks
- Interface down → validate link state, controlled flap, escalate if recurring
- BGP session down → validate neighbor state, controlled session restart
- Certificate expiring in 30 days → renew, deploy, verify per policy
- DNS resolution failure → validate resolver, clear cache per policy
- ACL misconfiguration → propose fix, approve, apply
Outcomes
- Faster triage with AI-assisted diagnosis
- Routine network fixes auto-executed with approval
- Certificate and config issues caught proactively
Guardrails used
RBAC • approvals • change window awareness • rollback • audit trail • quarantine after failures
Change window protection
Context / problem
- Rollbacks eat the night—manual, error-prone
- Drift during change window goes undetected
- Freeze periods not consistently enforced
How Resolvify helps
- Understand: Monitor during change window for drift, failure, and health degradation
- Resolve: Trigger rollback per policy; block execution during freeze; validate post-change stability
- Improve: Learn which changes cause issues; tune rollback triggers; quarantine failing patterns
Example runbooks
- Deploy drift detected → rollback to last known good
- Health check failure post-deploy → automatic rollback per policy
- Config drift during freeze → block, alert, no execution
- Database migration failure → rollback migration, restore, notify
- Canary failure → traffic shift back per policy, alert
Outcomes
- Rollbacks in minutes, not hours
- Change windows respected—no automation during freeze
- Guardrails catch drift before it becomes an incident
Guardrails used
RBAC • approvals • change window awareness • rollback • audit trail • quarantine after failures
What to automate first
Top 10 ideal first candidates
These incidents are high-volume, repeatable, and low-risk—ideal for early automation with Resolvify.
Impact vs. risk matrix
Where to focus early automation—high impact, lower risk first.
| Impact | Risk | Examples | Recommend |
|---|---|---|---|
| High | Low | Password reset, service restart, disk cleanup | Early automation |
| High | Medium | JVM OOM, pod restart, interface bounce | Early automation |
| Medium | Low | Cert renewal, Lambda concurrency | Early automation |
| Medium | Medium | RDS connections, config drift | Scale later |
Adoption pattern
How different teams use Resolvify
From CIO to L1—every role gets value. Here's how each team engages.
CIO / Head of Infra
Dashboard, guardrails, compliance
Visibility into automation coverage, approval workflows, and audit trails. Set policies; stay in control.
SRE / Ops
Author, approve, tune runbooks
Create and refine runbooks, approve high-risk executions, tune guardrails. Own the automation strategy.
NOC / L1
Auto-triage, fewer tickets
Benefit from auto-triage and resolution. Focus on edge cases instead of repeat incidents.
Start with one high-impact use case
Don't boil the ocean. Pick L1 automation or cloud ops—we'll help you prove value fast, then expand.
Social proof
Use case results
Real outcomes tied to specific use cases. Anonymized, but real.