This article outlines an intern task to create a module-based course on Agentic AI, including modules, simple explanations with diagrams, slides, assignments, quizzes, and 2–3 mini projects. The structure, deliverables per module, and required assets are described so the course can be produced consistently across ten example modules and compiled into clean notes and slide decks.
Course Overview and Structure
This module-based course on Agentic AI consists of ten example modules designed to be taught sequentially. Each module includes simple explanations (with diagrams), slides per module, one easy and one practical assignment, short quizzes, and a deliverable quiz bank. The ten modules are:
- What is Agentic AI? (Intro, use cases)
- How Agents Work (Perception → Reasoning → Action)
- Memory for Agents (short-term, long-term)
- Planning & Decision Making
- Agents + Tools (APIs, web search, etc.)
- Agents with LLMs (LangChain, AutoGPT basics)
- Multi-Agent Systems (agents talking to each other)
- Reinforcement Learning for Agents (basic)
- Safety & Ethics (risks, alignment)
- Industry Applications + Projects
Each module is accompanied by a simple diagram where relevant. Examples of diagram content (as provided):
- How Agents Work: a flow diagram showing Perception → Reasoning → Action.
- Memory for Agents: a layered diagram showing short-term and long-term memory.
- Agents + Tools: a diagram mapping agents to APIs, web search, and external tools.
- Multi-Agent Systems: a connectivity diagram illustrating agents talking to each other.
- Agents with LLMs: a diagram noting LangChain and AutoGPT basics as tool integrations.
Module Deliverables and Course Assets
For consistent production, each module must include the following deliverables and assets:
- Notes: simple PDF with diagrams summarizing the module.
- Slides: 10–12 slides per module. Slide decks should cover: introduction, core concepts, the module diagram, assignment overview, quiz pointers, and references to tools or examples listed in the module title.
- Assignments: two per module — one easy and one real-world/practical assignment.
- Quizzes: short quizzes for learning; deliverable includes a quiz bank of 100 questions per module.
Slide guidance per module (aligned to given module headings):
- What is Agentic AI? — slides for Intro, Use cases, Diagram, Notes, Assignment, Quiz.
- How Agents Work — slides for Perception, Reasoning, Action, Flow Diagram, Assignment, Quiz.
- Memory for Agents — slides for Short-term, Long-term, Memory Diagram, Assignment, Quiz.
- Planning & Decision Making — slides for Planning concepts, Decision flow, Diagram, Assignment, Quiz.
- Agents + Tools — slides for APIs, Web search, Tool integration, Diagram, Assignment, Quiz.
- Agents with LLMs — slides for LangChain, AutoGPT basics, Integration diagram, Assignment, Quiz.
- Multi-Agent Systems — slides for Communication patterns, Coordination diagram, Assignment, Quiz.
- Reinforcement Learning for Agents — slides for basic RL concepts, Environment interaction, Assignment, Quiz.
- Safety & Ethics — slides for Risks, Alignment, Mitigation diagram, Assignment, Quiz.
- Industry Applications + Projects — slides for Applications, Project outlines, Mini project links, Assignment, Quiz.
Assignments per module should follow the stated pattern: one easy task to build foundational understanding and one real-world practical task that relates to the module topic. Quizzes should be short for immediate feedback, while the deliverable quiz bank provides 100 questions per module for assessment flexibility.
Mini projects: the course includes 2–3 mini projects aligned with the Industry Applications + Projects module and designed to integrate multiple modules' deliverables.
Notes on Packaging and Outputs
- Produce a simple PDF notes file for each module containing the module diagram and concise explanations.
- Construct 10–12 slide decks per module following the slide guidance above.
- Prepare assignment briefs: label one as easy and one as real-world practical for every module.
- Assemble short quizzes for learning and a 100-question quiz bank per module for deeper assessment.
Use this structure to create consistent, module-based course materials that match the intern task brief and the listed module topics and deliverables.
Conclusion
This intern task specifies a structured, module-based Agentic AI course composed of ten example modules, each with simple explanatory notes and diagrams, 10–12 slides, one easy plus one practical assignment, short quizzes, and a 100-question quiz bank per module. The course also includes 2–3 mini projects tied to industry applications. Follow these deliverables to assemble complete notes, slides, assignments, quizzes, and project outlines.
