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
各ステージには授業、報酬ポイント、通関ボーナスがあり、段階的に卒業へ進みます。
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
0
Lessons
0/20
Stages
0/5
Assignments
0/5
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
0%
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
0%
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.