DevOps with AI

Your staging deploy is green, but production 500s the moment real traffic hits. The runbook is three engineers’ tribal knowledge, your Terraform state drifted from reality months ago, and the one person who understands the Helm values is on vacation. DevOps is where small mistakes compound into outages — and where an AI assistant that can read your manifests, query your metrics, and draft a remediation plan changes how fast you recover.

These guides show how to use Cursor, Claude Code, and Codex across the deployment lifecycle: generating pipelines and infrastructure code, debugging failing clusters, and running incident response. Each one is built around copy-paste prompts and the failure modes that generic tutorials skip.

Workflows in this section

CI/CD Pipelines Generate and debug GitHub Actions, GitLab CI, and deployment workflows — with the gotchas around caching, matrix builds, and rollback.

Containerization Write lean multi-stage Dockerfiles, debug Kubernetes manifests, and have AI explain why a pod is CrashLooping.

Infrastructure as Code Produce production-ready Terraform modules and import existing resources, with security and cost review built into the loop.

Incident Response Triage alerts, find root cause from logs and metrics, and draft a remediation plan and postmortem under pressure.

Performance Tuning Profile slow services, right-size resource limits and autoscalers, and validate the fix before it ships.

Where MCP servers change the workflow

For DevOps, the difference between a generic answer and a useful one is whether the assistant can see your actual state. Model Context Protocol (MCP) servers give Cursor, Claude Code, and Codex live, read-only access to your toolchain, so instead of pasting log dumps you let the agent query the source. MCP setup is identical across all three tools — point them at the same server config.

A few that are mature and worth wiring up in 2026:

AWS Labs MCP

The awslabs/mcp suite — separate servers for AWS documentation, CDK guidance, cost analysis, and core prompt handling. Run each via uvx and add only the ones your team uses.

Datadog MCP

Datadog’s MCP server reached general availability in March 2026. It feeds live logs, metrics, and traces into the agent during an investigation — under your existing access controls.

Grafana MCP

grafana/mcp-grafana lets the agent search dashboards, run PromQL/LogQL queries, and pull incident context directly from your Grafana instance.

Terraform MCP

hashicorp/terraform-mcp-server gives the agent current Registry provider docs and module metadata so generated HCL targets real arguments. Still beta — keep it out of production-write paths.

Read-only first

Connect observability and documentation MCP servers (Datadog, Grafana, AWS docs) before anything that can mutate infrastructure. The biggest early wins are in investigation and code generation, where the agent reads state and you stay in control of what actually gets applied.

Security guidance moves fast — verify before you ship it

Kubernetes PodSecurityPolicy was removed in v1.25 (2022); any prompt or generated manifest that references PSP will not apply on a current cluster. Ask for Pod Security Admission with the built-in Privileged/Baseline/Restricted standards plus NetworkPolicy instead. The same caution applies to other hardening advice: treat anything the AI generates as a draft to validate against current provider docs, not a finished control.

What’s next

Pick the workflow closest to what’s on fire right now. Each guide opens with a concrete scenario and ends with copy-paste prompts and a “When This Breaks” section. For broader operations coverage, see the related guides below.

Monitoring & Observability Instrument services and build dashboards and alerts that catch problems before users do.

Cost Optimization Find and cut cloud waste with AI-assisted analysis of usage and right-sizing.

Security Operations Harden infrastructure and automate security review with current, verifiable controls.

Compliance Automation Turn SOC2, PCI, and audit requirements into checks the AI can help you maintain.