{"product_id":"designing-and-deploying-ai-agents-architectures-protocols-and-case-studies","title":"Designing and Deploying AI Agents: Architectures, Protocols, and Case Studies","description":"\u003cdiv\u003e\u003cp\u003e Artificial Intelligence has entered the era of agentic systems—software entities capable of perceiving, reasoning, planning, acting, and learning. This course provides a rigorous and practical foundation for designing, building, and deploying modern AI agents in real-world environments. Over three days, participants will learn the core architectures and protocols behind intelligent agents, build agents that use tools, memory, retrieval, and multi-step reasoning, implement MCP and Agent-to-Agent (A2A) communication, debug, test, and deploy agentic systems, and apply skills to role-based real-world case studies in an applied workshop. This course blends theory, engineering practice, and hands-on development to create production-ready agent solutions.\u003c\/p\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch3\u003eDesigning and Deploying AI Agents: Architectures, Protocols, and Case Studies Benefits\u003c\/h3\u003e\n\u003cul\u003e\u003cli\u003e\n\u003cp\u003e\u003cb\u003eCourse Benefits\u003c\/b\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eOrganizations struggle to move beyond basic LLM integrations (chatbots, summarizers) to autonomous, multi-step agentic systems that can reason, plan, and use tools reliably in production environments\u003c\/li\u003e\n\u003cli\u003eDevelopers and architects lack practical knowledge of emerging agent communication protocols (MCP, A2A) and multi-agent orchestration patterns, leading to fragile, unreliable agent pipelines\u003c\/li\u003e\n\u003cli\u003eTeams face critical challenges in debugging, evaluating, and safely deploying agents—including hallucinations, broken plans, tool-selection failures, and lack of observability and governance frameworks\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cb\u003e \u003c\/b\u003e\u003cstrong\u003ePrerequisites\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eExperience with Python. Basic familiarity with APIs and JSON.\u003c\/li\u003e\n\u003cli\u003eComfort working in Linux\/VM environments.\u003c\/li\u003e\n\u003cli\u003eFamiliarity with LLMs or ML concepts is helpful but not mandatory.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/li\u003e\u003c\/ul\u003e\n\u003c\/div\u003e\u003cdiv\u003e\u003ch3\u003eDesigning and Deploying AI Agents Training Outline\u003c\/h3\u003e\u003c\/div\u003e\u003cdiv\u003e\n\u003ch4\u003eLearning Objectives\u003c\/h4\u003e\n\u003cp\u003e\u003cstrong\u003eDAY 1 — Foundations of Designing AI Agents\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eModule 1: Introduction to Modern AI Agents\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eFrom LLM applications to agentic systems\u003c\/li\u003e\n\u003cli\u003eSingle-agent vs multi-agent patterns\u003c\/li\u003e\n\u003cli\u003eAgent maturity levels\u003c\/li\u003e\n\u003cli\u003eCore agent capabilities: perception, reasoning, acting, learning\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 2: The Cognitive Loop \u0026amp; Agent Development Lifecycle\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePerceive → Interpret → Reason → Act → Learn\u003c\/li\u003e\n\u003cli\u003eMapping cognition to implementation building blocks\u003c\/li\u003e\n\u003cli\u003eAgent development lifecycle: Requirements → Architecture → Build → Test → Deploy → Monitor\u003c\/li\u003e\n\u003cli\u003eLab 1: Build a Minimal Cognitive Loop Agent\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 3: Agent Architectures \u0026amp; Memory Systems\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePlanner–Executor models\u003c\/li\u003e\n\u003cli\u003eWorking memory and long-term memory\u003c\/li\u003e\n\u003cli\u003eSemantic memory via vector DBs\u003c\/li\u003e\n\u003cli\u003eLab 2: Add Memory to an Agent\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 4: The Art of Agent Prompting\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eSystem, developer, user prompt separation\u003c\/li\u003e\n\u003cli\u003eRole\/Persona engineering\u003c\/li\u003e\n\u003cli\u003eChain-of-Thought and Tree-of-Thought prompting\u003c\/li\u003e\n\u003cli\u003eLab 3: Prompt Engineering for Agents\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDAY 2 — Advanced Architectures, Protocols \u0026amp; Deployment\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eModule 5: MCP \u0026amp; Agent-to-Agent Protocols\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe role of protocols in agent reliability\u003c\/li\u003e\n\u003cli\u003eMCP: tools, resources, schemas, contexts\u003c\/li\u003e\n\u003cli\u003eA2A: message envelopes, metadata, routing\u003c\/li\u003e\n\u003cli\u003eLab 4: Build an MCP-Enabled Agent\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 6: Multi-Agent Orchestration\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhen multi-agent systems outperform single agents\u003c\/li\u003e\n\u003cli\u003ePlanner–Executor–Verifier topologies\u003c\/li\u003e\n\u003cli\u003eLab 5: Planner + Executor Multi-Agent Workflow\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 7: Agentic Workflows\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eAgent vs workflow vs hybrid models\u003c\/li\u003e\n\u003cli\u003eHuman-on-the-loop and human-in-the-loop patterns\u003c\/li\u003e\n\u003cli\u003eIntegrating agents into existing business processes\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 8: Evaluating \u0026amp; Debugging Agents\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTool-selection failures, hallucinations, broken plans\u003c\/li\u003e\n\u003cli\u003eTrace-based debugging workflows and behavioral test suites\u003c\/li\u003e\n\u003cli\u003eLab 6: Debug a Misbehaving Agent\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eModule 9: Deploying Agents into Production\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeploying as APIs via FastAPI\u003c\/li\u003e\n\u003cli\u003eObservability, logging, security hardening, and governance\u003c\/li\u003e\n\u003cli\u003eLab 7: Deploy an Agent Using FastAPI\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eDAY 3 — Applied Workshop (“Choose Your Channel”)\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cp\u003eParticipants select a single track aligned with their professional role:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eTrack A: The Data Analyst (BI Agent) — Build an agent that transforms raw data into insights using pandas, matplotlib\/seaborn\u003c\/li\u003e\n\u003cli\u003eTrack B: The Software Engineer (Coding Agent) — Build a test-driven code-generation agent with iterative refinement\u003c\/li\u003e\n\u003cli\u003eTrack C: The Enterprise Operator (Service\/Chat Agent) — Build a context-aware enterprise chatbot with RAG and escalation\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003eLab 8 (Capstone): Domain-Specific Deployment\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003ePackage the agent into an API or deployment target\u003c\/li\u003e\n\u003cli\u003eHandle a surprise scenario introduced by the instructor\u003c\/li\u003e\n\u003cli\u003eTest, refine, and optionally demo your final solution\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e","brand":"Learning Tree","offers":[{"title":"267A94CN \/ 2026-07-08T09:00:00 \/ Online","offer_id":53522218123630,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"267C41EN \/ 2026-07-29T09:00:00 \/ London","offer_id":53522218156398,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"268D23US \/ 2026-08-26T09:00:00 \/ Herndon, VA","offer_id":53522218189166,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"269C53EN \/ 2026-09-16T09:00:00 \/ London","offer_id":53522218221934,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"26AA49CN \/ 2026-10-07T09:00:00 \/ Ottawa","offer_id":53522218254702,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"26AB59EN \/ 2026-10-28T09:00:00 \/ London","offer_id":53522218287470,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"26BC87US \/ 2026-11-23T09:00:00 \/ Herndon, VA","offer_id":53522218320238,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"26CB48EN \/ 2026-12-16T09:00:00 \/ London","offer_id":53522218353006,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"271A67CN \/ 2027-01-06T09:00:00 \/ Ottawa","offer_id":53522218385774,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"272B90US \/ 2027-02-24T09:00:00 \/ Herndon, VA","offer_id":53522218418542,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"274A57CN \/ 2027-04-07T09:00:00 \/ Ottawa","offer_id":53590004826478,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"271B89EN \/ 2027-01-27T09:00:00 \/ London","offer_id":53733644173678,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"273B73EN \/ 2027-03-17T09:00:00 \/ London","offer_id":53733644206446,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"274B44EN \/ 2027-04-28T09:00:00 \/ London","offer_id":53733644239214,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true},{"title":"275C65US \/ 2027-05-26T09:00:00 \/ Herndon, VA","offer_id":53835173396846,"sku":"US-1376-IL","price":0.0,"currency_code":"USD","in_stock":true}],"url":"https:\/\/learningtreeinternationalsita.myshopify.com\/products\/designing-and-deploying-ai-agents-architectures-protocols-and-case-studies","provider":"SITA Training","version":"1.0","type":"link"}