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Building an Agent — From Idea to Production

Extend the existing presentation “Agentic AI for Beginners — A Primer” with an additional 20-slide module titled: “Building an Agent — From Idea to Production” This new module m…

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The idea behind this presentation

Extend the existing presentation “Agentic AI for Beginners — A Primer” with an additional 20-slide module titled: “Building an Agent — From Idea to Production” This new module must move the audience from understanding WHAT an agent is to understanding HOW an agent is actually conceived, designed, assembled, equipped, tested, deployed, operated, evaluated, secured, and continuously improved. The audience should finish this module with a practical lifecycle mental model: Problem → Goal → Agent Specification → Model Selection → Tool Selection → Skills → MCP / Integrations → Knowledge & Memory → Instructions → Orchestration → Build → Test → Evaluate → Secure → Deploy → Observe → Improve Do not turn this into a vendor-specific tutorial. Teach the underlying engineering principles first, then show representative tools, MCPs, skills, platforms, registries, SDKs, and marketplaces as examples. CONTEXT: Audience: - Engineering students - Engineering/Technology faculty - Beginners interested in actually building AI agents - Learners who may have used ChatGPT or other LLM applications but have not built an agent Assume: - Basic programming literacy - Basic understanding of APIs - No prior experience with MCP, agent frameworks, tool calling, orchestration, vector databases, agent skills, or production deployment Pedagogical goal: Move from conceptual understanding to practical system-design thinking. The module must answer these questions explicitly: 1. How do I identify a problem that genuinely requires an agent? 2. How do I define the agent's goal? 3. How do I specify what the agent is allowed to do? 4. How do I select the right model? 5. What tools does the agent need? 6. How do I discover/procure those tools? 7. What are “skills” in an agentic system? 8. How are skills different from tools? 9. What is MCP? 10. How does MCP relate to tools, resources, and prompts? 11. Where can I find MCP servers/integrations? 12. How do I connect an agent to external systems? 13. How do I provide knowledge to an agent? 14. How do I provide memory/state? 15. How do I define permissions? 16. How do I test whether the agent actually works? 17. How do I evaluate an agent systematically? 18. How do I secure it? 19. How do I deploy and monitor it? 20. How do I continuously improve it? IMPORTANT CONCEPTUAL DISTINCTIONS: Do not blur these concepts together. Clearly distinguish: MODEL = The reasoning/generation capability. PROMPT / INSTRUCTIONS = Guidance governing the model's behavior. TOOL = A callable capability that lets the system perform an operation or access an external system. SKILL = A reusable capability or procedure that packages domain knowledge/instructions/workflows for accomplishing a class of tasks. Explain that “skill” is not a universally standardized term and its exact meaning varies by agent platform. MCP = A protocol/ecosystem for connecting AI applications to external capabilities and context. Explain its role without implying that MCP is itself an agent, model, or universal tool marketplace. RESOURCE / KNOWLEDGE = Information made available to the agent. MEMORY / STATE = Information maintained across steps or interactions. ORCHESTRATION = The control logic coordinating model calls, tools, state, permissions, and execution. AGENT = The system combining these components into a goal-directed execution loop. Do not claim that every agent requires every one of these components. INSTRUCTIONS: Create exactly 20 additional slides. These slides should follow the original primer naturally and function as a practical “Agent Builder” module. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 23 — FROM “WHAT IS AN AGENT?” TO “HOW DO I BUILD ONE?” ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Purpose: Transition from conceptual understanding to implementation. Visual: Show: UNDERSTAND ↓ DESIGN ↓ EQUIP ↓ BUILD ↓ TEST ↓ DEPLOY ↓ OPERATE ↓ IMPROVE Key message: “Building an agent is systems engineering, not simply writing a clever prompt.” Introduce the lifecycle that the next 19 slides will follow. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 24 — STEP 1: START WITH THE PROBLEM, NOT THE AGENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Teach: Do not begin with: “I want to build an AI agent.” Begin with: “What task requires flexible decision-making, tool use, iteration, or adaptation?” Show a decision framework: Is the task: - Goal-oriented? - Multi-step? - Variable? - Tool-dependent? - Difficult to fully specify with rules? - Worth the additional complexity? If mostly NO → use simpler automation/software. If mostly YES → an agent may be appropriate. Include: “Use the simplest architecture that reliably solves the problem.” ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 25 — STEP 2: DEFINE THE AGENT'S MISSION ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Teach how to convert a vague objective into an agent specification. Example: BAD: “Build a research agent.” BETTER: “Find recent peer-reviewed literature on battery thermal management, summarize relevant findings, identify research gaps, and produce a cited research brief.” Define: Goal Inputs Expected output Constraints Success criteria Failure conditions Human approval requirements Visual: Agent Mission Specification Card. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 26 — STEP 3: DEFINE THE AGENT'S BOUNDARIES ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Teach: An agent should not merely know what it CAN do. It should know what it MUST NOT do. Create: AGENT PERMISSION MODEL Can: ✓ Search approved sources ✓ Read specified files ✓ Run analysis ✓ Draft reports Cannot: ✕ Delete production data ✕ Send external communications without approval ✕ Modify critical systems ✕ Access unrelated confidential information Introduce: Least privilege Allowed tools Allowed data Allowed actions Approval gates Budget/time limits Key message: “Capabilities should be explicitly bounded.” ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 27 — STEP 4: CHOOSE THE MODEL ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Explain that model selection depends on the task. Compare dimensions: Reasoning capability Tool calling Context window Latency Cost Multimodal capability Structured output Reliability Privacy/deployment requirements Show: Simple task → smaller/faster model Complex planning → stronger reasoning model High-volume workflow → optimize cost/latency Sensitive environment → consider deployment/privacy requirements Do not recommend one vendor universally. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 28 — STEP 5: DISCOVER WHAT TOOLS THE AGENT NEEDS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Introduce “tool inventory.” Ask: What must the agent: READ? SEARCH? CALCULATE? CREATE? MODIFY? EXECUTE? COMMUNICATE? MONITOR? Example: Research Agent: - Web/search - Academic databases - PDF/document reader - Citation manager - Spreadsheet - Code execution Software Agent: - Git repository - Issue tracker - Code execution - Test runner - Documentation - CI/CD Create a visually engaging “Agent Toolbox.” ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 29 — TOOL CATEGORIES ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Create a taxonomy: INFORMATION - Search - Databases - Documents - Knowledge bases COMPUTATION - Python - Calculators - Simulators - Data-analysis engines ACTION - APIs - Email - Ticketing - CRM - ERP ENGINEERING - CAD - Simulation - Git - CI/CD - IoT OBSERVATION - Logs - Monitoring - Sensors - Analytics Explain: A tool should exist because it provides a capability the model cannot reliably perform itself. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 30 — TOOL vs SKILL vs KNOWLEDGE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ This must be one of the clearest conceptual slides. Use a three-column comparison: TOOL “Something the agent can call.” SKILL “A reusable capability/procedure for accomplishing a type of task.” KNOWLEDGE “Information the agent can use.” Example: Tool: Python execution Skill: “Analyze a CSV dataset for anomalies.” Knowledge: “Company's vibration-analysis standards.” Show how all three can combine. Explicitly note: “Skill” has different meanings across agent platforms; use the term carefully and define it in the architecture being used. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 31 — WHERE DO AGENT SKILLS COME FROM? ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Answer the user's practical question directly. Show five sources: 1. BUILD YOUR OWN - Internal procedures - Python/TypeScript code - API wrappers - Domain workflows 2. OFFICIAL PLATFORM ECOSYSTEMS - Agent SDKs - Tool libraries - Integration frameworks 3. OPEN-SOURCE PROJECTS - Git repositories - Community-maintained libraries - Open agent components 4. MCP ECOSYSTEMS / SERVERS - Community and organization-provided integrations 5. COMMERCIAL INTEGRATIONS / MARKETPLACES - SaaS integrations - Enterprise connectors - Specialized APIs Important: Do not present any marketplace or registry as inherently trustworthy. Teach learners to evaluate: - Maintainer - Documentation - Permissions - Security - Source code - Update frequency - Dependencies - Data handling - License - Reputation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 32 — WHAT IS MCP? ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Explain MCP from first principles. Use an analogy: Without a common protocol: Every AI application ↔ every tool requires a custom integration. With a common protocol: AI application ↕ MCP ↕ External capabilities/context Explain the basic role of MCP as a standardized way for AI applications to interact with external servers that expose capabilities/context. Do not describe MCP as: - An AI model - An agent - A replacement for APIs - A magical universal integration layer Introduce the concepts only at an appropriate beginner level: - MCP host - MCP client - MCP server - Tools - Resources - Prompts ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 33 — MCP: HOW IT FITS INTO THE AGENT STACK ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Create a layered architecture: USER ↓ AGENT APPLICATION ↓ MODEL ↓ ORCHESTRATION ↓ MCP CLIENT ↓ MCP SERVER(S) ↓ TOOLS / RESOURCES ↓ EXTERNAL SYSTEMS Show examples: MCP server → Git repository MCP server → Database MCP server → Files MCP server → Search MCP server → Business system Clarify: MCP provides a standardized connection mechanism; the agent application still determines what to call, when to call it, and under what permissions. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 34 — HOW TO FIND / PROCURE MCP SERVERS AND INTEGRATIONS ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Create a practical discovery map. Potential sources: - Official MCP documentation/ecosystem resources - Official vendor integrations - Organization-maintained repositories - Trusted open-source repositories - Internal enterprise MCP servers - Integration platforms - Community directories/registries For each source explain: WHAT IT PROVIDES WHO MAINTAINS IT WHAT TO VERIFY Add a prominent warning: “Never connect an unknown MCP server to sensitive data or powerful tools without security review.” Explain procurement criteria: - Authentication - Authorization - Permissions - Data residency - Logging - Security posture - Licensing - Support - Reliability - Versioning - Vendor/community reputation Use current web research to identify representative, legitimate MCP ecosystems and sources. Do not invent registries or integrations. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 35 — BUILD vs BUY vs CONNECT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Create a decision matrix. BUILD: When: - Unique domain capability - Internal process - Specialized engineering logic BUY: When: - Commodity capability - Mature SaaS integration - Strong vendor support CONNECT: When: - Existing API/MCP server already provides the capability Decision criteria: Cost Security Time Reliability Customization Maintenance Vendor dependency Key lesson: “Do not build infrastructure that already exists and is trustworthy—but do not blindly connect third-party capabilities either.” ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 36 — STEP 6: GIVE THE AGENT KNOWLEDGE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Show: INSTRUCTIONS + DOCUMENTS + DATABASES + RAG + LIVE INFORMATION Explain: - What the agent needs to know - Where that information lives - How current it must be - How it should retrieve it Example: University research agent: Research policies Course documents Research papers Laboratory manuals Institutional databases Explain the difference between: Knowledge access and Action capability. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 37 — STEP 7: GIVE THE AGENT MEMORY & STATE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Explain: Agents often need to remember what happened during a task. Show: TASK STATE → current objective → completed steps → pending steps → tool results → errors → decisions LONGER-TERM MEMORY → user preferences → recurring information → historical interactions EXTERNAL KNOWLEDGE → documents → databases → RAG Emphasize: Memory must be deliberately designed. Do not assume the model automatically has persistent memory. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 38 — STEP 8: DESIGN THE ORCHESTRATION ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Show the agent controller: Goal ↓ Plan ↓ Select action ↓ Call tool ↓ Observe ↓ Evaluate ↓ Continue / Retry / Replan / Escalate Explain: Orchestration is the control logic connecting the model, tools, state, and environment. Introduce framework-neutral concepts: - Tool routing - State management - Retry logic - Conditional branches - Parallel execution - Human approval - Timeouts - Error handling ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 39 — STEP 9: ACTUALLY BUILD THE AGENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Give the audience a practical build sequence: 1. Create agent specification 2. Select model 3. Define instructions 4. Add tools 5. Add knowledge/RAG 6. Add state 7. Define permissions 8. Implement orchestration 9. Add verification 10. Add human approval 11. Log execution Show conceptual pseudocode: agent = create_agent(...) agent.add_tool(...) agent.add_knowledge(...) agent.set_permissions(...) agent.set_success_criteria(...) run(task) while not complete: observe() decide() act() verify() Do not make this framework-specific. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 40 — WHAT DEVELOPMENT TOOLS DO WE USE? ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Answer directly: “What tools do I use while creating an agent?” Create categories: CODING - Python - TypeScript/JavaScript MODEL ACCESS - Model APIs - SDKs AGENT DEVELOPMENT - Agent SDKs/frameworks - Orchestration libraries TOOL INTEGRATION - REST APIs - Function/tool calling - MCP KNOWLEDGE - Vector databases - Search - Document stores TESTING - Unit tests - Integration tests - Agent evaluations OBSERVABILITY - Logs - Traces - Metrics DEPLOYMENT - Containers - Cloud platforms - Serverless infrastructure Make clear that frameworks change rapidly. Focus on architectural categories first. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 41 — STEP 10: TEST THE AGENT BEFORE TRUSTING IT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Teach that: “A successful demo is not the same thing as a reliable agent.” Create test categories: NORMAL CASES EDGE CASES AMBIGUOUS TASKS TOOL FAILURE WRONG DATA MISSING DATA ADVERSARIAL INPUT PERMISSION VIOLATION LONG TASK RECOVERY FROM ERROR Include: - Unit tests - Integration tests - Scenario tests - Regression tests Explain why agentic systems require repeated evaluation. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 42 — STEP 11: EVALUATE THE AGENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Define agent evaluation dimensions: Task success Accuracy Tool selection Tool-call correctness Plan quality Recovery ability Safety Latency Cost Consistency Human escalation User satisfaction Show an example evaluation: 100 test tasks → 82 completed successfully → 9 required human intervention → 6 failed due to tool selection → 3 produced incorrect outputs Explain that evaluation should be measurable and repeatable. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 43 — STEP 12: SECURE THE AGENT ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Create a security architecture: USER ↓ AGENT ↓ POLICY / PERMISSION LAYER ↓ TOOLS ↓ EXTERNAL SYSTEMS Cover: - Authentication - Authorization - Least privilege - Secrets management - Sandboxing - Prompt-injection defenses - Data protection - Tool validation - Audit logs - Rate limits Use this principle: “An agent should have only the permissions necessary to accomplish its job.” ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 44 — STEP 13: DEPLOY, OBSERVE & IMPROVE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Show: BUILD ↓ TEST ↓ DEPLOY ↓ OBSERVE ↓ EVALUATE ↓ IMPROVE ↺ Observe: - Tool calls - Errors - Latency - Cost - Success rate - Human interventions - Security events Explain: Production agents require continuous monitoring because environments, tools, data, models, and user behavior change. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 45 — THE COMPLETE AGENT LIFECYCLE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Create a visually powerful lifecycle diagram: 1. PROBLEM ↓ 2. GOAL ↓ 3. SPECIFICATION ↓ 4. MODEL ↓ 5. TOOLS ↓ 6. SKILLS ↓ 7. MCP / INTEGRATIONS ↓ 8. KNOWLEDGE ↓ 9. MEMORY / STATE ↓ 10. ORCHESTRATION ↓ 11. BUILD ↓ 12. TEST ↓ 13. EVALUATE ↓ 14. SECURE ↓ 15. DEPLOY ↓ 16. OBSERVE ↓ 17. IMPROVE ↺ Show that this is iterative rather than a one-way pipeline. Add: “Production is not the end of the lifecycle.” ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 46 — YOUR FIRST AGENT: A COMPLETE EXAMPLE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Bring everything together. Example: “Engineering Research Assistant” GOAL: Create a technically grounded research brief. TOOLS: Search PDF/document retrieval Citation extraction Python Spreadsheet SKILLS: Literature review Data analysis Citation checking Report generation KNOWLEDGE: University research guidelines Approved databases Reference documents MCP / INTEGRATIONS: Use representative current integrations where appropriate. MEMORY: Research question Sources reviewed Findings Open questions GUARDRAILS: No fabricated citations No unsupported claims Human review before publication OUTPUT: Verified research brief Show the entire architecture visually. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ SLIDE 47 — THE AGENT BUILDER'S CHECKLIST ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Create a practical checklist students/faculty can reuse: □ Is an agent actually necessary? □ Is the goal measurable? □ Are success criteria defined? □ Are tools necessary? □ Are tools trustworthy? □ Are skills clearly defined? □ Where will skills come from? □ Is MCP useful? □ Are MCP servers trusted? □ What knowledge is required? □ What memory/state is required? □ What permissions are necessary? □ Where is human approval required? □ How will the agent be tested? □ How will it be evaluated? □ How will it be monitored? □ What happens when it fails? End with: “Build the smallest trustworthy agent that solves the problem.” OUTPUT CONTRACT: Produce exactly 20 additional slides numbered 23–47, but note that this range contains 25 numbers; therefore resolve the numbering conflict by using exactly 20 slides numbered 23–42, while incorporating the lifecycle concepts from the proposed later slides through intelligent consolidation. Recommended final 20-slide sequence: 23. From “What Is an Agent?” to “How Do I Build One?” 24. Start With the Problem 25. Define the Agent's Mission 26. Define Boundaries & Permissions 27. Choose the Model 28. Discover the Required Tools 29. Tool Categories 30. Tool vs Skill vs Knowledge 31. Where Do Agent Skills Come From? 32. What Is MCP? 33. MCP in the Agent Stack 34. Finding / Procuring MCP Servers & Integrations 35. Build vs Buy vs Connect 36. Give the Agent Knowledge 37. Give the Agent Memory & State 38. Design the Orchestration 39. Actually Build the Agent 40. Development Tools for Building Agents 41. Test, Evaluate & Secure the Agent 42. Deploy → Observe → Improve + Complete Lifecycle + First-Agent Example + Builder Checklist For Slide 42, create a visually rich final lifecycle synthesis rather than overcrowding it with text. Use a second appendix-style slide only if the presentation tool automatically permits more than 20 additional slides. WEB RESEARCH REQUIREMENT: Because MCP ecosystems, agent SDKs, skill repositories, integration platforms, and available tooling evolve rapidly, use current web research for the sections dealing with: - MCP - MCP servers - MCP registries/directories - Agent SDKs/frameworks - Agent skill repositories - Tool marketplaces - Current development tooling Prioritize: 1. Official protocol/documentation sources 2. Official vendor documentation 3. Reputable open-source repositories 4. Established technical communities 5. Independent technical sources For every current ecosystem/tool example, verify that it actually exists and is relevant. Do not invent: - MCP marketplaces - MCP servers - Agent skill stores - APIs - SDK capabilities - Framework features - Integration capabilities Clearly distinguish: OFFICIAL COMMUNITY THIRD-PARTY EXPERIMENTAL When discussing “where to procure skills,” explain that there is no single universal marketplace or standardized procurement mechanism for all agent skills. Skills may be developed internally, packaged as software/workflows, exposed through tools or MCP servers, obtained from platform ecosystems, or sourced from third-party/open-source repositories depending on the architecture. QUALITY CONTROLS: Before producing the slides, verify: 1. The lifecycle is technically coherent. 2. “Tool,” “skill,” “knowledge,” “memory,” “MCP,” and “agent” are not conflated. 3. MCP is described accurately and without hype. 4. Current examples are verified. 5. Security is integrated into the lifecycle rather than appended at the end. 6. Procurement/discovery includes security and trust evaluation. 7. Students can understand how an agent is actually built. 8. Faculty can translate the lifecycle into teaching/research projects. 9. The deck does not become a catalog of commercial products. 10. Vendor examples are illustrative, not endorsements. 11. The audience understands that agent frameworks and ecosystems evolve quickly. 12. The audience understands that an agent is a system, not merely a prompt. 13. The audience understands that more tools do not necessarily make an agent better. 14. The audience understands that autonomy must be proportional to risk. 15. Every consequential action has an appropriate verification or human-approval concept. 16. The final lifecycle is iterative: Design → Build → Test → Deploy → Observe → Improve → Redesign. VISUAL DESIGN: Maintain the visual language of the original “Agentic AI for Beginners — A Primer” deck. Use: - Engineering architecture diagrams - Lifecycle diagrams - Decision trees - Tool maps - Comparison matrices - Layered system diagrams - Checklists - Workflow diagrams - Realistic engineering examples Avoid: - Generic AI robot imagery - Futuristic decoration - Marketing-style product grids - Excessive logos - Dense text - Unverified ecosystem screenshots The visual centerpiece of this module should be the AGENT LIFECYCLE. FINAL TEACHING MESSAGE: “Building an agent is not: Prompt + LLM = Agent. It is closer to: Problem + Goal + Model + Instructions + Tools + Skills + Knowledge + State + Orchestration + Permissions + Verification + Evaluation + Monitoring = A trustworthy agentic system.” End with the question: “If you had to build one agent for your engineering department tomorrow, what problem would you give it—and what would you explicitly NOT allow it to do?”

Building an Agent — From Idea to Production

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