AI Agent Tutorial
Broad tutorial intent from learners who want a structured path to build and ship agents.
- ai agent tutorial
- ai agent course
- how to build ai agents
Moi stage gom bai hoc, diem so va phan thuong khi hoan thanh.
You must pass this lesson exam before the next lesson unlocks. Exams include multiple-choice and true/false questions.
Pass rule: score at least 67% to clear this lesson.
Total Points
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Lessons
0/20
Stages
0/5
Assignments
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Each stage of the curriculum now lines up with tutorial-style search intent, so the page can capture both broad AI agent course queries and specific implementation long tails.
Broad tutorial intent from learners who want a structured path to build and ship agents.
High-fit intent tied to tool invocation, schema design, and agent execution contracts.
Model Context Protocol remains a strong tutorial cluster for tool integration and context plumbing.
Search intent is strongly implementation-focused around orchestration, state machines, and multi-agent design.
Security long tails are specific and actionable, which makes them good targets for lessons and checklists.
Teams searching for production readiness want testing, evals, traces, latency, and success-rate guidance.
Lv.1 · STAGE-1
Understand how a Skill is structured and how inputs, outputs, and execution rules work.
Stage Progress
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Stage bonus +120 points
Learn the boundary between Skills, Prompts, Tools, and Workflows.
Cover inputs, outputs, dependencies, and error handling as the four essentials.
Avoid blindly reusing unknown scripts and build a whitelist and audit habit.
Build an engineering loop of change, verification, and rollback.
Lv.2 · STAGE-2
Break tasks into reusable Skills to reduce token cost and failure rate.
Stage Progress
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Stage bonus +180 points
Turn natural-language requests into structured tool interfaces.
Build a controllable multi-layer prompting structure.
Add schema validation and privilege boundaries to each Skill.
Use versioning and compatibility rules to avoid production blowups.
Lv.3 · STAGE-3
Split complex problems into coordinated tasks across multiple agents.
Stage Progress
0%
Stage bonus +220 points
Define nodes, state transitions, and failure recovery in a graph.
Learn planner, executor, and reviewer collaboration chains.
Use low-code orchestration and visual debugging for agent workflows.
Build the three core health signals for an agent system.
Lv.4 · STAGE-4
Upgrade a Skill from runnable to maintainable, reviewable, and production-ready.
Stage Progress
0%
Stage bonus +280 points
Combine unit tests, scenario tests, and regression tests.
Set a release gate for risk before publishing.
Lower per-run cost and make operating cost predictable.
Define a release SOP that can recover from production mistakes.
Lv.5 · STAGE-5
Complete a full capstone project and build a reusable Agent Skill portfolio.
Stage Progress
0%
Stage bonus +350 points
Define success criteria around an actual problem.
Ship retrieval, execution, and review Skills as a set.
Produce a test report and a security report.
Turn the project into a reusable public case study.
Each stage has at least one assignment. Treat every submission as a checkpoint and record what failed and how you fixed it.