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Agentic Devops with Claude Code
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 15h 6m | Size: 2.05 GB
Early Access - Covers Agent Skills, Subagents, Hooks, MCP, GitOps, CI/CD, Kubernetes, Observability, FinOps, Guardrails
What you'll learn
Build AI agents that do real DevOps work - Kubernetes triage, Terraform authoring, release gates, incident response - against a real broken system
Engineer the context an agent runs on: scoped instruction files, evidence contracts, and bounded queries that stop a firehose
Choose model and effort per role as a staffing decision, and prove the bill with an OpenTelemetry run ledger
Compile a runbook into an Agent Skill, then test it against positive, negative and ambiguous cases
Pick the right tool surface for each job - raw CLI, wrapper script, Agent Skill or MCP server - and justify the choice
Enforce boundaries with PreToolUse hooks, scoped RBAC identities and GitOps, then prove they still hold after you delete the hook
Design role-specific subagents with mechanically tested authority, and orchestrate them without three agents echoing one guess
Triage a degraded Kubernetes cluster, rank faults by blast radius, and remediate through a separate write identity
Wire an agent into CI that emits a schema-validated release verdict and holds no deploy authority of its own
Run an incident on bounded evidence, falsify the obvious wrong hypothesis, and produce an evidence packet you can defend
Rank security findings by reachability instead of raw CVSS, and block a deploy on the agent's own verdict
Run a governed agent unattended against a real Alertmanager alert, with the properties an always-on loop actually needs
Requirements
Working knowledge of Git and the command line
Comfortable with Docker and Docker Compose
Experience with CI/CD pipelines - GitHub Actions or equivalent
Kubernetes basics: workloads, services and probes
Terraform basics: plans and state
Familiarity with metrics, logs and SLOs
A Claude Pro or Max subscription - every command in this course runs on a subscription, no Anthropic API key required
A machine with 8 GB RAM and 20 GB free disk (16 GB RAM and 40 GB recommended), with Docker running
No prior Claude Code experience needed - it is taught from zero
Description
Most AI-for-DevOps material stops at "ask Claude to write the YAML." This course does not.
You build agents that do real operations work on a real, broken system - and the guardrails that make them safe to point at production.
The spine isNorthstar Commerce. You join its platform team and inherit one flawed repository: two services, a fault-injectable upstream, a Terraform estate, Helm charts, CI, Prometheus and Alertmanager, open incident tickets, and scripts that inject faults and grade your work. Every module improves that same repo. Nothing here is a toy example.
What you build
- A triage agent that reads a live Kubernetes cluster, ranks five simultaneous faults by blast radius, and remediates through a second identity the first one structurally cannot use
- An IaC engineer that authors a Terraform module test-first and prices the change before it ships
- A release gate that decides whether a candidate ships, emits a schema-validated verdict your pipeline branches on, and holds no deploy authority at all
- An incident investigator that produces an evidence packet where every claim names the command that produced it - and declares what it did not check
- A four-role security team that ranks scanner findings by reachability instead of raw CVSS
- An always-on agent wired to a real Alertmanager alert
What you learn around them
The agents are the easy part. The operating layer is the course.
- Context as a budget, not a bucket - scoped instruction files, evidence contracts, bounded queries
- Model and effort as a staffing decision, with an OpenTelemetry run ledger to prove the bill
- Runbooks compiled into Agent Skills that a role can actually invoke - and tested against positive, negative and ambiguous cases
- Tool interfaces chosen deliberately: raw CLI, wrapper script, Skill or MCP server
- Hooks that enforce where a prompt only asks - and RBAC that still holds after you delete the hook
- Role-specific subagents with mechanically tested authority, and orchestration that does not collapse into three agents echoing one guess
How it is taught
Every module is a Lesson, a Lab, a Quiz, and a Deep Dive where the topic earns one. Labs are copy-runnable and validated live against the real stack before publish, so when a page shows you an output, that output is real - including the run that cost $0.34 and the ones that fail. Claims are measured, not asserted. Where a control does not actually hold, the course says so and shows you the measurement that proves it.
Every command runs on a Claude Pro or Max subscription. You do not need an Anthropic API key.
Who this is for
DevOps, SRE, platform, Kubernetes and DevSecOps engineers with working experience in Git, containers, CI/CD, Terraform and Kubernetes. Claude Code is taught from zero. The DevOps stack is not.
Early access
Modules 1 to 14 are live now - from first principles through context and cost engineering, skills, hooks, subagents and orchestration, into Kubernetes triage, release engineering and incident response. Agentic SecOps, always-on agents, agent evaluation and the capstone are built and land next. Enrol now and every new module is yours as it ships, at the early-access price.
Who this course is for
DevOps and SRE engineers who want agents doing real ops work, not chat-assisted YAML editing
Platform engineers building the guardrails their team's agents will run inside
Kubernetes and cloud engineers who want AI help with triage without handing it production write access
DevSecOps engineers tired of working through scanner output ranked by raw CVSS score
Engineering leads deciding how much autonomy to grant an agent, and how to prove the boundary holds
Anyone who tried AI for operations, got a confident wrong answer, and wants the discipline that catches it
Not for engineers new to DevOps - this course assumes the stack and teaches the agents on top of it
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