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Create a polished, university-quality presentation titled: “Agentic AI for Beginners — A Primer” Design the presentation specifically for Engineering students and Engineering/Te…
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Create a polished, university-quality presentation titled: “Agentic AI for Beginners — A Primer” Design the presentation specifically for Engineering students and Engineering/Technology Faculty. The deck should take a complete beginner from “What is AI?” to a clear, practical understanding of Agentic AI, including how agents perceive, decide, use tools, act, observe results, verify outcomes, and operate under human oversight. The presentation must educate rather than promote. Avoid AI hype, vague buzzwords, and vendor marketing language. CONTEXT: Audience: - Undergraduate and postgraduate Engineering students. - Engineering, Technology, Computer Science, and interdisciplinary faculty. - Mixed technical backgrounds. - Assume basic digital literacy but little or no prior knowledge of AI agents. Pedagogical level: - Beginner-friendly first. - Engineering-oriented throughout. - Progressively introduce technical concepts. - Explain jargon immediately. - Use familiar engineering analogies and real-world examples. - Provide enough technical depth for faculty without overwhelming beginners. Central teaching idea: “An LLM can generate an answer. An agentic system can pursue a goal through a controlled cycle of deciding, using tools, observing results, and adapting its next action.” Build the entire presentation around this conceptual transition: AI → Generative AI → LLM → Chatbot / Assistant → Tool Use → Planning → Memory / State → Action → Observation → Verification → Agentic AI Use the supplied reference material as conceptual grounding where relevant, especially its treatment of agents, tool use, decomposition, prompting, evaluation, security, and safety. Do not turn the presentation into a prompt-engineering lecture. INSTRUCTIONS: 1. CREATE A STRONG OPENING Do not begin with a dictionary-style definition. Begin with a compelling engineering scenario: “A student asks an AI: ‘Analyze this machine vibration dataset and tell me whether there may be a fault.’ A chatbot can explain how to perform the analysis. An agentic system could: 1. Locate the dataset. 2. Inspect its structure. 3. Select appropriate analysis tools. 4. Run calculations/code. 5. Detect anomalies. 6. Generate findings. 7. Verify important results. 8. Prepare a report. 9. Ask a human for approval before any consequential action.” Use this scenario to introduce the difference between generating information and pursuing a goal through actions. 2. BUILD THE PRESENTATION AS A LEARNING JOURNEY Use approximately 20 slides with this narrative: 01 — Title 02 — What You Will Learn 03 — Why Agentic AI Matters 04 — The Engineering Scenario 05 — From AI to Agentic AI 06 — What Exactly Is an AI Agent? 07 — Chatbot vs Assistant vs Workflow vs Agent 08 — Anatomy of an Agent 09 — The Agent Loop 10 — Tools: How Agents Act on the World 11 — Planning & Task Decomposition 12 — Memory, State & RAG 13 — Verification & Human-in-the-Loop 14 — Single-Agent vs Multi-Agent 15 — Engineering Use Cases 16 — Education & Faculty Use Cases 17 — Where Agents Fail 18 — Security, Safety & Governance 19 — When NOT to Use an Agent 20 — How to Build Your First Agent 21 — Learning Roadmap 22 — Key Takeaways + Interactive Quiz If the presentation generator works better with 20 slides, intelligently merge related sections while preserving the learning progression. 3. DEFINE THE CORE TERMS Explain these progressively: AI Generative AI Large Language Model (LLM) Chatbot AI Assistant / Copilot Workflow Automation AI Agent Agentic AI Agentic System Multi-Agent System Use a visual hierarchy rather than presenting ten isolated definitions. Explicitly state: “Not every application powered by an LLM is an agent.” Avoid implying that “agentic” always means fully autonomous. 4. EXPLAIN THE AGENT ARCHITECTURE Create a clean, professional architecture diagram: ┌──────────────────┐ │ GOAL │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ AGENT / MODEL │ │ Decide / Plan │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ TOOLS │ │ APIs / Code / DB │ │ Search / Systems │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ OBSERVATION │ │ Results / State │ └────────┬─────────┘ ↓ ┌──────────────────┐ │ EVALUATE / VERIFY│ └────────┬─────────┘ │ Continue / Correct │ └──────────↺ Include Human Approval as a control point for high-impact or irreversible actions. Clearly distinguish: - Model - Instructions - State/memory - Tools - Environment - Control loop - Evaluation - Human oversight 5. TEACH THE AGENT LOOP Use the simple engineering mental model: SENSE ↓ THINK / PLAN ↓ ACT ↓ OBSERVE ↓ EVALUATE ↓ ADAPT ↺ Explain that this resembles a feedback loop conceptually, but do not claim that an AI agent is literally a classical control system. Show a simple worked example: Goal: “Diagnose an abnormal motor reading.” Observe → retrieve specifications → analyze data → identify anomaly → verify → produce recommendation → request human approval. 6. EXPLAIN TOOLS Show tools visually as an “agent toolbox”: 🔎 Search 🧮 Calculator 💻 Code execution 🗄 Database 📁 Files 🔌 APIs 📊 Analytics 🌐 Web 🏭 Enterprise systems 🤖 Physical devices/robots Explain: “The model decides what may need to happen; tools provide capabilities for actually doing things.” Explain why tool access increases both capability AND risk. 7. EXPLAIN PLANNING Introduce: - Task decomposition - Sequencing - Conditional decisions - Iteration - Replanning - Error recovery Use an engineering example: “Design a small solar monitoring system.” Break it into: Requirements → components → calculations → simulation → validation → documentation. Make clear that planning generated by an AI can be wrong and therefore should be evaluated. 8. EXPLAIN MEMORY, STATE AND RAG Use a three-layer visual: CURRENT CONTEXT ↓ WORKING STATE / MEMORY ↓ EXTERNAL KNOWLEDGE / RAG Explain clearly: RAG = obtaining relevant information. Tools = performing actions. Memory/state = maintaining information needed across steps. Agent = coordinating these capabilities toward a goal. Do not suggest that RAG itself is an agent. 9. EXPLAIN HUMAN-IN-THE-LOOP Create a spectrum: Fully manual → AI assistance → AI recommendation → AI action with approval → Limited autonomy → Higher autonomy Explain why higher autonomy requires stronger controls. Use examples: Low risk: Draft an email. Medium risk: Modify a software repository. High risk: Control industrial equipment. Very high consequence: Make decisions affecting safety, finances, or people. Emphasize: “The more consequential the action, the stronger the human control and verification should be.” 10. EXPLAIN MULTI-AGENT SYSTEMS Use a visual team metaphor: Research Agent ↓ Planning Agent ↓ Coding Agent ↓ Testing Agent ↓ Review Agent Explain potential benefits: - Specialization - Parallel work - Separation of responsibilities Also explain disadvantages: - Coordination overhead - Higher cost - Higher latency - More complex failure propagation - More difficult debugging Do not portray multi-agent systems as automatically better. 11. INCLUDE ENGINEERING USE CASES Show 5–6 visually distinct use cases: Software Engineering: Requirements → Code → Test → Review Data Engineering: Data → Clean → Analyze → Validate → Report Mechanical Engineering: Sensor data → Diagnose → Simulate → Recommend Electronics: Symptoms → Documentation → Tests → Diagnosis Research: Question → Search → Retrieve → Analyze → Synthesize → Verify Education: Learning objective → Research → Explain → Quiz → Feedback For each, show: GOAL INPUT TOOLS ACTIONS VERIFICATION HUMAN OVERSIGHT OUTPUT 12. INCLUDE A FACULTY PERSPECTIVE Create a dedicated section for faculty: Teaching: - AI teaching assistants - Personalized explanations - Automated formative feedback Research: - Literature discovery - Data analysis - Coding assistance - Research workflow support Curriculum: - Agentic AI projects - AI literacy - Responsible AI - Engineering applications Assessment: - Process-based evaluation - Oral defense - Project demonstrations - AI-use disclosure Academic integrity: - Teach responsible AI use rather than assuming AI can simply be prohibited. 13. INCLUDE FAILURE MODES Create a visual “Failure Map”: Wrong information ↓ Wrong decision ↓ Wrong tool call ↓ Wrong action ↓ Cascading error Cover: - Hallucination - Incorrect planning - Goal misinterpretation - Tool misuse - Prompt injection - Data leakage - Excessive permissions - Cascading failures - Weak verification - Non-determinism - Automation bias - Cost/latency escalation 14. INCLUDE SECURITY AND SAFETY Use the engineering principle: “Capability without control creates risk.” Explain: - Least privilege - Sandboxing - Authentication - Authorization - Tool validation - Input/output validation - Logging - Monitoring - Human approval - Rate limiting - Rollback - Recovery - Prompt-injection defenses Keep this accessible to beginners but technically credible. 15. INCLUDE “WHEN NOT TO USE AGENTS” This is essential. Compare: Deterministic software → Best when rules are known and predictable. Simple automation → Best for repeatable workflows. LLM prompting → Best for language-centric tasks. RAG → Best when grounded knowledge retrieval is central. Single agent → Best when flexible tool use and iteration are useful. Multi-agent → Best when specialization/coordination genuinely adds value. Key lesson: “Use the least complex architecture that reliably solves the problem.” 16. INCLUDE A CONCEPTUAL BUILD-YOUR-FIRST-AGENT SLIDE Show: 1. Define the goal 2. Define success 3. Define allowed actions 4. Select model 5. Connect tools 6. Define state 7. Add verification 8. Add human approval 9. Test 10. Monitor 11. Improve Include framework-neutral conceptual pseudocode: while task_not_complete: observe() decide_next_step() if tool_needed: call_tool() evaluate_result() if approval_required: request_human_approval() update_state() Do not present this as production code. 17. INCLUDE AN INTERACTIVE SCENARIO Ask the audience: “An AI system is asked to diagnose a malfunctioning industrial machine. Should it: A. Simply explain the possible fault? B. Retrieve technical documentation? C. Analyze sensor data? D. Take corrective action? E. Request human approval before action? Discuss why the correct answer may depend on risk, permissions, and system design.” Reveal the engineering principle rather than simply giving a single answer. 18. INCLUDE A LEARNING ROADMAP Create a visual staircase: LEVEL 1 AI + GenAI Fundamentals LEVEL 2 LLMs + Prompting LEVEL 3 APIs + Tools + Structured Outputs LEVEL 4 RAG + Knowledge Systems LEVEL 5 Agent Architecture LEVEL 6 Evaluation + Security + Governance LEVEL 7 Advanced / Multi-Agent Systems 19. VISUAL DESIGN Create a sophisticated university/engineering aesthetic. Preferred visual language: - Modern academic - Clean technical diagrams - Minimalist - Professional - High readability - Strong hierarchy - Subtle visual storytelling - Consistent iconography - Engineering-inspired diagrams - Generous whitespace Avoid: - Generic humanoid AI robots - Excessive futuristic imagery - Cyberpunk/neon aesthetics - Stock-photo clichés - Decorative visuals with no teaching value - Dense paragraphs - Tiny text Use diagrams whenever a process, architecture, relationship, or comparison can be visualized. 20. SLIDE CONTENT RULE Every slide must have: A clear title + One central idea + A short takeaway + A meaningful visual Do not overcrowd slides. Use speaker notes for deeper explanations. 21. SPEAKER NOTES For every major technical slide, provide concise speaker notes that explain: - What the instructor should say - Why the concept matters - A useful analogy - A potential misconception to correct Speaker notes should add value rather than repeat the slide. 22. SOURCING Use the supplied reference material where relevant. The presentation should accurately reflect concepts concerning: - Prompting - Decomposition - Agents - Tool-use agents - Code-generation agents - Observation-based agents - RAG - Evaluation - Security - Alignment Do not invent citations or statistics. If current external information is needed, use web search selectively and prioritize: - Primary research - Official documentation - Universities - Standards organizations - Authoritative technical sources Do not allow web search to turn the presentation into vendor marketing. OUTPUT CONTRACT: Generate the final presentation directly as a polished 20–22 slide deck. For each slide: - Slide title - 3–6 concise content elements - Visual/diagram - Speaker notes - Sources when needed Also include: - Title slide - Table of contents - Learning objectives - Interactive scenario - Comparison table - Engineering examples - Risk/failure map - Learning roadmap - Final quiz FINAL SLIDE: End with these three memorable ideas: 1. “An LLM generates.” 2. “An agent acts toward a goal.” 3. “A trustworthy agent is controlled, observable, verifiable, and appropriately supervised.” Then ask: “Where would you use an agent — and where would you deliberately NOT use one?” QUALITY CONTROLS: Before generating the deck, verify: - Is it genuinely understandable to a beginner? - Can a student explain the difference between an LLM, chatbot, workflow, and agent? - Is the agent loop visually clear? - Are tools, planning, memory, RAG, and verification distinguished? - Are autonomy claims appropriately qualified? - Are engineering examples realistic? - Are risks integrated into the architecture rather than treated as an afterthought? - Is human oversight clearly represented? - Does the deck serve both students and faculty? - Is every slide visually understandable? - Is there minimal unnecessary text? - Are technical claims defensible? - Are unsupported facts avoided? - Is the presentation educational rather than promotional? - Does the final deck leave the audience with a practical mental model? DESIGN PRIORITY: When forced to choose between: more information vs clearer understanding, choose clearer understanding. When forced to choose between: decorative visuals vs explanatory visuals, choose explanatory visuals. When forced to choose between: complex architecture vs understandable architecture, choose understandable architecture. The final presentation should feel like a carefully designed introductory lecture from a leading engineering professor and AI-systems architect, suitable for delivery to a university classroom, faculty development program, AI-awareness workshop, or engineering seminar.
Presentation overview
This community presentation was shared by Raja Jeevan Kumar Maduri2 as a complete 20-slide example. Create a polished, university-quality presentation titled: “Agentic AI for Beginners — A Primer” Design the presentation specifically for Engineering students and Engineering/Te… The preview lets you review the full sequence in order, rather than judging the design from a single cover image.
Use it as a reference for planning your own deck: notice how the amount of information changes from slide to slide, where visual emphasis appears, and how repeated design choices help the presentation feel connected. Community examples are inspiration, not locked templates, so you can keep the ideas that fit your audience and replace anything that does not.
Learn from the example
Look at the job each slide performs. Identify where the deck introduces its subject, develops the main points, adds evidence or examples, and moves toward a conclusion.
Notice which element attracts attention first and how headings, supporting text, images, and data are separated. Strong hierarchy makes the intended reading order obvious.
Compare spacing, typography, color, and repeated components across the slides. Consistent rules help varied content feel like one presentation instead of unrelated screens.
Make it your own
Begin with your audience and the decision, lesson, or action the presentation should support. Replace the topic, examples, evidence, and visuals with material you can verify, then edit every slide for one clear takeaway. You can borrow the visual direction without copying the creator's wording or message. Presenton will use this community deck as a design reference while you develop content for your own purpose.
