Play 37
AI-Powered DevOps
Medium✅ Ready
Intelligent SRE — incident triage, automated runbooks, deployment risk scoring, predictive scaling.
AI-assisted SRE and DevOps platform combining intelligent incident triage, automated runbook execution, deployment risk scoring, GitOps with AI review, and predictive scaling. Azure OpenAI analyzes incidents and generates remediation plans. Azure Monitor provides telemetry, Azure DevOps and GitHub Actions handle CI/CD, and Container Apps hosts the SRE agent. Reduces mean-time-to-resolution by automating first-responder investigation and runbook execution.
Architecture Pattern
AI SRE: incident triage, runbook automation, risk scoring, predictive scaling
Azure Services
Azure OpenAI (gpt-4o)Azure MonitorAzure DevOpsGitHub ActionsContainer Apps
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — DevOps Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (108 lines), evaluate (107 lines), tune (103 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with DevOps + Monitor key inputs + envFile
TuneKit (AI Config)
- config/openai.json — incident analysis prompts
- config/devops.json — severity rules, runbook triggers, scaling thresholds
- config/guardrails.json — blast radius limits, approval gates
- evaluation/eval.py — Resolution accuracy >85%, MTTR reduction >40%
Tuning Parameters
Severity classification rulesRunbook configScaling thresholdsBlast radius limitsApproval gate policies
Estimated Cost
Dev/Test
$80–200/mo
Production
$1K–4K/mo