A beginner-friendly course that helps you understand how AI agents move from answering questions to completing tasks, and how tools, sources, memory, workflows, guardrails, and evaluation fit together.
Distinguish ordinary AI chat from an agent, and explain how an agent advances a task toward a goal.
Explain the role of task instructions for an agent, and judge whether the goal, role, boundaries, and output requirements are clear.
Distinguish tasks that only need generated answers from tasks that need tool support, and explain the value and risk of tool calling.
Explain how source grounding improves agent answers, and judge when retrieval, files, or a knowledge base are needed.
Distinguish conversation history, task state, and long-term preferences, and explain how memory helps an agent continue a task.
Break a complex agent task into goal, steps, inputs, outputs, tools, and checkpoints.
Judge whether a task truly needs multiple agents, and explain responsibility, tool, risk, and handoff boundaries.
Identify input, output, tool, and human-confirmation boundaries for an agent scenario.
Use fixed test cases, process tracing, and an improvement loop to judge whether an agent is stable and reliable.
Combine goal, task instructions, sources, tools, memory, workflow, guardrails, and evaluation into a minimal agent blueprint.
