Generative AI and Agentic AI for DevOps Engineer

Objective: Build 3 autonomous agents that run your infrastructure: monitoring, fixing, and optimizing.

INFRASTRUCTURE DRIFT & INCIDENT REMEDIATION

Learning: Design & Deploy

Infrastructure Drift Detection & Auto-Fix Agent

• Trigger: Hourly or on-demand
• What it does:
  1. Read current cloud state (AWS/GCP API)
  2. Compare to Terraform desired state
  3. Detect drift and security violations
  4. Generate remediation code
  5. Apply fix (with approvals)
  6. Validate and report
• Outcome: Infrastructure always matches code
• Direct impact: No more “drift creep”

Incident Auto-Remediation Agent

• Trigger: CloudWatch alert threshold exceeded
• What it does:
  1. Ingest alert + logs
  2. Diagnose root cause (AI analysis)
  3. Execute safe runbook (restart, scale, restart DB)
  4. Validate fix
  5. Report resolution to PagerDuty/Slack
• Outcome: 70% of incidents auto-resolved in <5 minutes
• Direct impact: On-call sleep uninterrupted

Build & Deploy Together

• Customize drift agent for YOUR cloud provider
• Connect to your CloudWatch/monitoring
• Customize incident agent with YOUR runbooks
• Deploy and test on staging incidents

Hands-on Lab 

• Deploy drift detection agent
• Deploy incident remediation agent
• Test both with sample scenarios
• Verify Slack/PagerDuty notifications

Deliverable

• Agents running on YOUR infrastructure

COST OPTIMIZATION AGENT

Learning: Design & Deploy

Cost Optimization Agent

• Trigger: Daily or weekly billing review
• What it does:
  1. Pull cloud billing data
  2. Analyze usage patterns
  3. Identify waste (unused resources, over-provisioning)
  4. Recommend optimizations (right-sizing, reserved instances, spot)
  5. Estimate ROI per recommendation
  6. Auto-apply low-risk optimizations
  7. Report monthly savings
• Outcome: 15-25% monthly cost reduction
• Direct impact: CFO gets happy

Build & Deploy Together

• Connect to your cloud billing APIs
• Configure budget alerts
• Customize optimization rules for YOUR workloads
• Test with real billing data
• Set up cost tracking dashboard

Hands-on Lab

• Deploy cost optimization agent
• Run against YOUR billing data
• Review cost reduction recommendations
• Auto-apply safe optimizations
• Set up cost dashboard and alerts

Deliverable

• Cost optimization running autonomously

Outcomes

• 3 autonomous agents deployed
• Drift detection running hourly
• Incidents auto-remediated
• Cost optimization identifying savings

Objective: Connect agents to your monitoring stack and security tools. Automate compliance and vulnerability response.

SECURITY & COMPLIANCE AUTOMATION

Content

Your Infrastructure Compliance Agent

• Trigger: Daily at 2am
• What it does:
  1. Run CIS compliance checks
  2. Scan for exposed secrets (env vars, configs)
  3. Check IAM permissions (too permissive?)
  4. Analyze vulnerability database
  5. Score findings by severity
  6. Auto-remediate low-risk issues
  7. Generate compliance report
• Outcome: 100% CIS compliance, vulnerabilities tracked to resolution

Tool Integration

• AWS Config + Security Hub
• Container image scanning (ECR/GCR scanning)
• OWASP scanning
• Dependency vulnerability checks

Build & Deploy Together

• Connect to your cloud security tools
• Configure compliance policies
• Set up remediation approval gates
• Deploy and test

Hands-on Lab

• Deploy security agent
• Run compliance scan on YOUR infrastructure
• Review remediation recommendations
• Set up compliance dashboard

CONNECTING YOUR OBSERVABILITY STACK

Content

Your Monitoring Stack Integration

• CloudWatch → Agent triggers
• DataDog/NewRelic → Agent reads metrics
• Jira → Agent creates tickets for issues
• PagerDuty → Agent escalates critical issues
• Slack → Agent posts status updates
• Email → Agent sends reports

Automation Patterns

• Threshold exceeded → Agent triggered
• Agent runs diagnosis → Auto-remediate if safe
• Manual approval required for risky changes
• Issue tracked in Jira
• Status posted to Slack
• Report emailed to leadership

Multi-Agent Workflows

• Agent 1 detects drift → Logs issue
• Agent 2 analyzes impact → Creates Jira ticket
• Agent 3 auto-fixes if safe → Updates ticket
• Agent 4 reports status → Notifies team

Scaling to Team

• Who gets alerted on incident
• Who can approve expensive operations
• Who sees cost reports
• Audit trail of all agent actions

Hands-on Lab

• Connect all agents to YOUR monitoring stack
• Test: Alert triggered → Agent runs → Issue resolved → Team notified
• Set up escalation paths
• Configure cost and security dashboards

Objective: Design and deploy a complete DevOps automation project. Something your team will depend on starting Monday.

DESIGN YOUR PROJECT

Requirements

• Solves a REAL operational pain point
• Uses ≥2 agents (incident + cost, or drift + security)
• Integrated with ≥2 cloud/monitoring tools
• Deployable and tested by end of day
• Something on-call engineer will use immediately

Example Projects

1. Incident Response Automation

• Input: CloudWatch alert
• Process: Agent diagnoses → Auto-remediates common issues → Reports
• Output: Incident resolved in Slack + Jira ticket
• Impact: 70% incidents resolved before human intervention

2. Infrastructure Audit & Remediation

• Input: Weekly schedule or on-demand
• Process: Agent audits all services → Finds drift/security issues → Fixes
• Output: Weekly compliance report + applied fixes
• Impact: Infrastructure always compliant

3. Cost Optimization Pipeline

• Input: Daily billing data
• Process: Agent analyzes spend → Recommends savings → Auto-applies safe changes
• Output: Weekly savings report + implemented cost cuts
• Impact: 20% monthly cost reduction

Your Project Design

• What operational pain point?
• What triggers automation?
• Which agents solve it?
• Which tools involved?
• What’s the team impact?

BUILD, TEST & DEPLOY

Hands-on Work

Step 1: Code & Configure (30 min)

• Start with agent templates
• Customize for YOUR infrastructure
• Add YOUR cost thresholds
• Add YOUR security policies
• Connect to YOUR cloud accounts

Step 2: Test in Staging (30 min)

• Simulate incident/drift/alert
• Verify agent runs correctly
• Check agent decisions (safe?)
• Verify notifications work
• Test rollback procedures

Step 3: Deploy to Production (20 min)

• Deploy agents to YOUR infrastructure
• Verify agent health
• Set up monitoring and alerts
• Document for team

Step 4: Present Impact (10 min)

• Show working automation
• Explain: problem → automation → outcome
• Demo: Trigger agent → Show result
• Share: Time saved and cost impact

Deliverables

• Fully working automation project
• Deployed in production
• Team trained on how it works
• Ready to use immediately