AI Free Microsoft MCP AI Agent Learning Plan: 2025 Training Guide October 14, 20258 views0 By IG Share Share Welcome to the definitive learning path for developers and AI engineers aiming to master Microsoft’s MCP for building sophisticated AI Agents. This comprehensive plan is designed to accelerate your skills, whether you’re looking for a rapid ramp-up or a deep dive into advanced concepts. Choose between our intensive 14-Day Accelerator to get up and running quickly, or our in-depth 6-Week Mastery track for comprehensive expertise. Each path is packed with hands-on labs, real-world deliverables, and a capstone project to solidify your learning and prepare you for enterprise-level AI development. MCP Learning Plan - User Driven UI Microsoft MCP AI Agent Learning Plan This learning plan helps you go from zero to production-ready with the Model Context Protocol (MCP) for AI agents. Pick the 14-Day Accelerator if you want a fast, hands-on path, or the 6-Week Mastery if you prefer deeper exploration and steady practice. Each day/week links to focused lessons and labs with real deliverables you can ship to stakeholders. Tip: Start with prerequisites below so your local environment and cloud access are ready before you begin. Prerequisites & Setup Accounts & Access GitHub account (for source control, CI and issues). Microsoft Entra ID access with permission to create app registrations (or admin to grant Graph scopes). Optional: Azure subscription (for Key Vault, Container Registry, App Service/Container Apps, Monitor). Optional: Microsoft 365 Developer tenant (Graph demo data), Jira test project (if integrating Jira). Optional: Microsoft Fabric trial or capacity (for Fabric tool labs). Tools & SDKs VS Code (latest) + REST client or curl. Node.js 18+ and npm/yarn; for TypeScript server/tools. Python 3.10+ (optional for Python tools), Git (latest). Docker Desktop (for containerizing servers/tools). Azure CLI & PowerShell 7+ (for auth, deploys), Terraform/Bicep (optional IaC). Postman or curl for quick API tests; OpenTelemetry CLI/collector (optional observability). Knowledge Prereqs JavaScript/TypeScript basics REST & JSON OAuth 2.0 / Entra ID JSON Schema Git & CI/CD Security fundamentals Helpful but optional: containers, OpenTelemetry, Azure Monitor/KQL, feature flags, canary deploys. Quick Setup Checklist Install Node 18+, Git, VS Code, Docker; verify with node -v, git --version, docker version. Login to Azure: az login; pick the right subscription: az account set --subscription "<name|id>". Create a Key Vault for secrets; enable developer-friendly secret loading in your server/tool. Register an Entra app for Graph with least-privileged scopes (Mail.ReadWrite, Calendars.ReadWrite, etc., as needed). Prepare a test Jira project (or stub) and a Fabric workspace (optional modules). Clone a starter MCP server; run locally and hit a health endpoint with curl. Select a track below. Mark lessons as complete. All lesson links open in a new tab. 14-Day Accelerator 6-Week Mastery 14-Day Accelerator (2–3 hours/day) Day Lesson Outcome / Lab Deliverable 1 MCP for Beginners + Intro to MCP Understand MCP & use case, draw sequence diagram. Problem statement, success metrics. 2 Core Concepts Servers, tools, schemas, sessions. Define schemas for 2 tasks. agents/specs/task-schemas.json 3 Security Best Practices Secret handling, scopes, audit. Security checklist, Key Vault script. 4 Build Your First MCP Server 1 local server, 2 tools, logging. Server, README, curl samples. 5 Deploy MCP Apps Wire a real tool, e.g. Graph (calendar, send mail draft). graph.ts, sample flows. 6 Advanced MCP Streaming, retries, batching, Jira tool with rate-backoff. jira.ts, retry middleware. 7 How to Contribute Conventions, CI, lint, test matrix. CONTRIBUTING.md, passing CI. 8 Lessons from Early Adopters Observability, logs, OpenTelemetry, KQL dashboards. observability.md, KQL queries. 9 Dev Best Practices Versioning, semantic-release, contract tests. Release pipeline, feature flags. 10 Case Studies in the Wild Enterprise adoption, threat modeling. ADR-001 (Arch. Decision Record). 11-12 Hands-On Labs in VS Code 1-click demo script, happy-path E2E demo. npm run demo, clean logs. 13 Hardening & Deployment Cloud deploy, containerize, infra, terraform. infra/bicep or terraform, notes. 14 Capstone Demo Stakeholder demo: Agent, Graph, Jira, Email flow. Demo video, README, perf/cost notes. 6-Week Mastery Track (5–7 hrs/week) Week Lesson(s) Focus / Lab Deliverable 1 1, 2, 3 MCP terminology, schema-first, 5 task designs. Design doc, sequence diagrams. 2 4, 5 Secrets, token flow, Managed Id, local server+Graph tool. Unit tests, local demo. 3 6 Add Fabric/Jira tools, transactional workflow, retries. Workflow state machine. 4 7 Advanced, chunking, multi-modal, concurrency, 50RPS load test. Load report, remediation plan. 5 8, 9, 10, 11 OSS PR, community, canary, feature flags, ADRs. Published OSS PR, ADRs. 6 12 Productionize, SLOs, runbook, capstone deploy/cloud. Capstone v1, README, runbook, SLOs. Research Topics & Advanced Modules Module Key Concepts Suggested Lab / Project Advanced Model Integration Fine-tuning LLMs, RAG vs. fine-tuning, evaluating model performance, using open-source models. Fine-tune a small open-source model (e.g., Llama 3 8B) on a domain-specific dataset and integrate it as an MCP tool. Autonomous Agents & Planning Chain-of-thought, ReAct framework, multi-agent systems (MAS), hierarchical planning. Build a research agent that can browse the web, summarize findings, and write a report on a given topic. Ethical AI & Guardrails Bias detection, content moderation, explainability (XAI), privacy-preserving techniques, implementing safety filters. Develop a set of configurable guardrails for an agent to prevent harmful or off-topic responses. Human-in-the-Loop (HITL) Approval workflows, interactive agent sessions, feedback loops for continuous learning. Implement a tool that requires user confirmation via email or Teams adaptive card before executing a critical action. Advanced Observability Distributed tracing, performance bottleneck analysis, cost tracking per agent session, automated alerting. Create a Grafana or Azure Monitor dashboard to visualize agent costs, latency, and error rates in real-time. Capstone Project & Resources Enterprise Tasking Agent with MCP: Summarize meetings (Graph), ops data (Fabric), create ticket (Jira), notifications (Outlook/Teams). Artifacts: Architecture diagrams, threat model, runbook, SLOs, demo video. Templates: Issue labels (feat, bug, ops, etc), ADR (Context, Decision, Consequence), Environment matrix. Stretch Goals: Tool sandbox, OpenAI integration, multi-tenant hardening, Azure Monitor dashboard. Weekly Routines: Plan/issue (Mon), Demo (Wed), Ship PR/summary (Fri), 5-min video wrap-up. Conclusion & Next Steps You now have two structured paths to build and ship MCP-based agents—plus advanced modules to deepen your practice. As you progress, keep deliverables small and shippable, automate checks (lint, tests, security), and measure cost, latency, and reliability with clear SLOs. Wrap with the capstone and record a short demo for stakeholders. If you chose Accelerator: Book a 30-min weekly checkpoint to demo progress and capture risks/decisions as ADRs. If you chose Mastery: Add load tests, canary flags, and a simple rollback plan before productionizing. After Capstone: Publish a reusable starter repo and create a runbook for ongoing ops. Pro tip: Keep secrets in Key Vault, apply least privilege, and log every external action an agent performs. Disclaimer: The Questions and Answers provided on https://gigxp.com are for general information purposes only. We make no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the website or the information, products, services, or related graphics contained on the website for any purpose. Share What's your reaction? Excited 0 Happy 0 In Love 0 Not Sure 0 Silly 0 IG Website Twitter
AI Guide to FP8 & FP16: Accelerating AI – Convert FP16 to FP8? The race to build larger and more powerful AI models, from massive language models to ...
AI Guide to FP16 & FP8 GPUs: Deep Dive Low-Precision AI Acceleration The world of artificial intelligence and high-performance computing is undergoing a seismic shift. As the ...
AI The Hidden Costs of Azure AI: A Deep Dive into Prompt Caching If you’re building with powerful models like Deepseek or Grok on Azure AI, you might ...
AI Seq2Seq Models Explained: Deep Dive into Attention & Transformers Sequence-to-Sequence (Seq2Seq) models have fundamentally reshaped the landscape of Natural Language Processing, powering everything from ...
Azure Azure AI Token Cost Calculator & Estimator | OpenAI & Foundry Models Planning your budget for an AI project? Our Azure AI Token Cost Estimator is a ...
AI Azure AI Search Tier & Sizing Calculator | Free Tool Choosing the right pricing tier for Azure AI Search can be complex. Balancing storage capacity, ...
AI Guide to Local LLM Deployment: Models, Hardware Specs & Tools The era of relying solely on cloud-based APIs for powerful AI is ending. A major ...
AI Gemini vs. GPT-5 vs. Perplexity: Reasoning vs Web vs Coding The generative AI landscape is no longer a one-horse race. With the launch of OpenAI’s ...
AI GPT-5 vs o3 & o4 mini: The AI Reasoning Comparison (2025) The world of AI is evolving, splitting between fast, all-purpose models like GPT-4o and deep, ...
AI GPT-5 vs. Thinking vs. Pro: The Ultimate Guide to OpenAI’s New AI (2025) OpenAI‘s launch of GPT-5 marks a monumental shift in artificial intelligence, but its new tiered ...
AI The MXFP4 Revolution: Your Ultimate Guide to 4-Bit AI Quantization The explosive growth of AI has hit the “memory wall,” where performance is limited not ...
AI Tech Giants’ $1 Trillion AI Datacenter Gamble – 2025 Investment Report The world is witnessing a capital investment cycle of historic proportions, a silent gold rush ...
