Stop Fighting Fires. Build Self-Healing Systems.
You're drowning in toil. Manual incident response, endless runbooks, alert fatigue. You know AI should automate this, but every tool wants production credentials in their cloud. CloudShip runs agents in your infrastructure—investigate incidents, correlate logs, auto-remediate—while you keep control.
Your Job Is 90% Toil, 10% Engineering
You became an SRE to build reliable systems. Instead, you're paged at 2am correlating logs manually. You write runbooks that break when infrastructure changes. You spend more time investigating incidents than preventing them.
Typical Incident Response
PagerDuty: API latency spike
P1 incident, customer-facing
Check DataDog. Database slow. Why?
Grep logs. Nothing obvious. Check deploys.
Find it: deployment 3 hours ago added N+1 query
Rollback. Wait for recovery. Write post-mortem.
45 minutes of manual investigation
Could've been automated
You became an SRE to build reliable systems. Instead, you're paged at 2am correlating logs manually. You write runbooks that break when infrastructure changes. You spend more time investigating incidents than preventing them.
The toil is infinite: manually correlating metrics across DataDog, logs in CloudWatch, deploys in GitHub, customer impact in Stripe. Every incident is the same investigation, different service. You know this should be automated.
Google's SRE book says toil should be <50%. Most SRE teams are at 70-80%. That's not sustainable.
Burnout, Attrition, Incidents
Your MTTR is measured in hours because every incident requires a human to investigate. Your team is burned out from on-call. Good SREs are leaving because they're glorified firefighters, not engineers.
| Your Metrics | With Automation |
|---|---|
| MTTR: 45+ minutes | MTTR: 5-10 minutes |
| Manual log correlation | Auto investigation |
| Runbooks break constantly | Agents adapt dynamically |
| 80% toil, 20% engineering | 30% toil, 70% engineering |
| Team burnout | Actual engineering work |
Your MTTR is measured in hours because every incident requires a human to investigate. Your team is burned out from on-call. Good SREs are leaving because they're glorified firefighters, not engineers.
Every manual incident investigation costs 30-60 minutes of someone's time. Multiply that by incidents per week, then multiply by engineer cost. You're burning six figures annually on toil that should be automated.
The real cost: You can't build reliability improvements because you're too busy fighting fires. That's the death spiral.
Autonomous Incident Response In Your Infrastructure
CloudShip Station runs agents in your environment. When incidents happen, agents automatically correlate logs, check recent deploys, analyze metrics, identify root cause. They post to Slack with context or auto-remediate based on your rules.
Without CloudShip (45+ min):
- Get paged, read alert
- Check DataDog dashboards
- Grep CloudWatch logs manually
- Check recent deployments
- Correlate timeline manually
- Form hypothesis, test, fix
- Write post-mortem
With CloudShip (5-10 min):
- Alert triggers agent workflow
- Agent correlates logs/metrics/deploys
- Posts to Slack with root cause
- Auto-remediate or await approval
- Generates post-mortem draft
CloudShip Station runs agents in your environment. When incidents happen, agents automatically correlate logs, check recent deploys, analyze metrics, identify root cause. They post to Slack with context or auto-remediate based on your rules.
Autonomous Investigation
Agents correlate logs, metrics, deployments, and customer impact automatically. No manual grepping. Results in Slack within minutes.
Controlled Remediation
You define what agents can fix automatically (restart service, scale pods) vs. what needs approval. Your runbooks, AI execution.
Learns Your Infrastructure
Agents understand your stack over time. They know which deploys are risky, which services are fragile, which customers are critical.
Deploy in your Kubernetes cluster. Credentials stay local. Audit every action.
What SRE Teams Automate First
These are the workflows that eliminate the most toil:
Incident Auto-Correlation
Alert fires → agent checks logs, recent deploys, metrics → posts root cause hypothesis to Slack. 45 min investigation → 3 minutes.
incident-correlate-agent
Auto-Remediation Workflows
Define safe fixes: restart pods, scale replicas, clear cache. Agent executes when patterns match. Reduce MTTR by 70%.
auto-heal-agent
Capacity Planning
Analyze usage trends, predict capacity needs, recommend scaling before you hit limits. Prevent incidents proactively.
capacity-agent
Post-Mortem Generation
Agent writes incident timeline, root cause, impact analysis, action items. You review and publish. Save hours per incident.
postmortem-agent
Runbook Execution
Convert static runbooks to agent workflows. They adapt when infrastructure changes. No more broken documentation.
runbook-agent
On-Call Context
When you get paged, agent already investigated. Slack thread has logs, metrics, recent changes. Start debugging, not searching.
oncall-context-agent
Deploy pre-built agents from our registry or build custom workflows for your runbooks. Your infrastructure, your rules, AI execution.
Stop Working in Silos
Right now: incident happens, you correlate manually. Was it a deploy? Check GitHub. Cost impact? Ask FinOps. Customer affected? Check Stripe. Takes 20 minutes just to gather context.
CloudShip Platform connects all your agents. When SRE agents investigate incidents, they automatically see recent deploys (DevOps), cost impact (FinOps), customer exposure (Product). Context that used to take 20 minutes is instant.
Example: Production incident
API latency spike → Incident agent sees: deployment 2hrs ago by @sarah, affects enterprise tier customers, costs $450/hr in SLA credits, similar pattern last month in staging. All in one Slack thread.
This is what platform means: all your operational data connected, contextualized, and actionable.
You Own The Infrastructure, We Provide The Intelligence
Open Source Runtime
Audit every agent action. Review prompts. Add guardrails. Security team approves because they can read the code.
Credentials Stay Local
Deploy in your VPC. AWS/GCP/GitHub credentials never leave your environment. CISO says yes.
Version Control Everything
Agents are code. Git workflow. PR reviews. Rollback. The DevOps practices you expect.
Reduce Toil. Build Reliability.
Cut MTTR by 70%. Automate incident investigation. Give your team time to do actual engineering.