Descriptions

AI Agent Engineer, AI Agent Engineer is a practical video course by Daniel Moka that teaches you how to build, steer, verify, and operate reliable AI coding agents using a real application. Across 10 modules, 55 video lessons, and 27 hands-on tasks, you will explore the fundamentals of AI agents, prompt and context engineering, harness engineering, memory engineering, loop engineering, and boundary engineering. You will learn to guide agents from your code repository using instruction files, skills, tools, Model Context Protocol (MCP), subagents, and hooks. The course also covers verification engineering, including mutation testing, property-based testing, end-to-end testing, testing legacy applications, and quality gates in continuous integration, as well as review engineering techniques such as evaluating plans before implementation, using skeptical reviewers, and reviewing pull requests efficiently.

You will also learn to create a centralized knowledge repository for agents, build local and online MCP servers, and implement automated workflows that can operate continuously while addressing risks such as prompt injection. The course culminates in building and running your own 24/7 AI employee on a machine or server, equipped with its own tools, memory, and assigned tasks. Included resources are the 90-page Agentic Coding Handbook with 9 chapters and 3 appendices, code examples, 20 diagrams, agent-executable guides, a hooks package, a configuration pack for the AI employee, a library of more than 10 practical skills, a second-brain template repository, and TicketBay, a Next.js and PostgreSQL practice application with a sandbox, a continuous integration gate, 27 practical tasks, and solutions. You receive lifetime access to the course and its updates.

What you’ll learn

• Understand the fundamentals of AI coding agents and how models, prompts, context, harnesses, memory, loops, and boundaries work together.
• Use prompt engineering and context engineering to improve the way AI agents perform coding tasks.
• Guide AI agents from a code repository using instruction files, skills, tools, MCP, subagents, and hooks.
• Verify AI-generated code using mutation testing, property-based testing, end-to-end testing, and continuous integration quality gates.
• Review AI-generated plans and code efficiently using skeptical reviewers, pull request tours, and triage techniques.
• Build a centralized knowledge repository that agents can share, maintain, and use.
• Build and configure local and online MCP servers to connect AI agents with tools and products.
• Create automated agent loops and email-based workflows while identifying and addressing prompt injection risks.
• Build and run a 24/7 AI employee on your own computer or server, with its own memory, tools, and assigned tasks.
• Apply the course concepts through 27 hands-on tasks using the TicketBay practice application built with Next.js and PostgreSQL.

Who this course is for

• Software engineers who already use AI coding agents and want their work to be more reliable and less dependent on constant supervision.
• Developers who ship software to production and want to verify AI-generated code with systematic testing and quality gates.
• Engineers who want to build automated AI workflows and run an AI employee continuously on their own computer or server.
• Developers using Claude Code, Cursor, or Codex who want to apply agent engineering principles across different tools.
• Software professionals interested in repository-based agent configuration, MCP tools, subagents, hooks, shared memory, and automated coding workflows.

Specificatoin of AI Agent Engineer

• Publisher : Ai Agent Engineer
• Teacher : Daniel Moka
• Language : English
• Level : All Levels
• Number of Course : 55
• Duration : 3 hours and 35 minutes

Content of AI Agent Engineer

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502 MB