On-call engineers face constant interruptions, sleep deprivation, and alert fatigue from managing production incidents 24/7. Current incident response relies heavily on manual intervention - engineers must wake up at 3 AM, analyze logs across multiple systems, diagnose issues, and execute remediation steps while under pressure.

How DreamOps Solves This

Core Features:

• Automated Incident Response: AI agent automatically triages and resolves common incidents (pod crashes, OOM kills, configuration issues) without human intervention
• Intelligent Root Cause Analysis: Uses Claude AI to analyze alerts with full context from Kubernetes, logs, metrics, and documentation
• Reduced MTTR: What typically takes 30-60 minutes of manual debugging can be resolved in 2-5 minutes automatically
• Context-Aware Decisions: Integrates with GitHub, Grafana, and Notion to understand your specific infrastructure and runbooks
• Sleep Protection: Engineers can actually rest while the AI handles routine incidents, only escalating complex issues

Real-World Impact:

• Saves 2-4 hours per on-call shift
• Reduces incident resolution time by 80% for common issues
• Prevents engineer burnout from repetitive tasks
• Maintains consistent remediation quality even at 3 AM

1. MCP Integration Complexity

Integrating multiple Model Context Protocol servers (Kubernetes, GitHub, Grafana) required building a robust abstraction layer. Each MCP server had different authentication methods and response formats.

• Solution: Created a unified

  MCPIntegration

  base class.

2. Safety vs Automation Balance

Implementing YOLO mode that can execute kubectl commands automatically was risky.

• Solution: Built a comprehensive risk assessment system that categorizes commands (low/medium/high risk) and only auto-executes with high confidence scores (≥0.8).

3. Real-time Webhook Processing

PagerDuty webhooks needed sub-second acknowledgment while Claude API calls take 2-4 seconds.

• Solution: Implemented async processing with immediate webhook acknowledgment and background analysis.

4. Context Window Management

With multiple integrations providing data, we hit Claude's token limits.

• Solution: Developed intelligent context prioritization - only fetching relevant logs/metrics based on the alert type.

5. Testing Kubernetes Failures

Creating realistic test scenarios was challenging.

• Solution: Built

  fuck_kubernetes.sh

  script that simulates various failure modes (CrashLoopBackOff, OOMKills, etc.) in isolated namespaces.