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AI agents & MCP curriculum — tools, safety, and delivery▌

For platform teams and product orgs rolling out agents beyond a PoC. Emphasizes evals, tool boundaries, and failure modes—matching how we teach agent skills on explainx.ai.

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instructional design: bloom’s taxonomy + measurable outcomes

Every module maps to explicit learning outcomes—not open-ended discussion without deliverables. We sequence along Bloom’s taxonomy (remember → understand → apply → analyze → evaluate → create): definitions and guardrails first, then applied exercises, then measurement and approvals. Facilitators run short checks for understanding after each block (2026 materials).

For organic and generative-engine visibility (GEO), we mirror patterns associated with stronger AI-search citation: answer-first sections, statistics where available, authoritative tone, clear H1–H3 structure, comparison tables when they reduce ambiguity, and FAQ blocks intended to pair with FAQPage JSON-LD. Teams produce briefs, scorecards, and checklists—not a generic “AI creativity” workshop.

program objectives

  • Separate demo stories from production agents with explicit tool scopes and timeouts.
  • Design MCP-style integrations with least-privilege patterns and observability hooks.
  • Stand up a minimal evaluation suite (tasks, graders, regression sets) before expanding features.
  • Connect engineers and PMs to shared vocabulary so leadership reviews stay technical, not mystical.

how we deliver

  1. 1

    Discovery call & problem framing

    We align on sponsors, success metrics, and constraints (2026 tool landscape, data rules, procurement gates) before anything is scheduled company-wide.

  2. 2

    Stakeholder interviews & day-in-the-life context

    Short conversations with practitioners (not only leadership) so scenarios reflect real workflows—not generic slide demos.

  3. 3

    Curriculum design & artifacts

    Modular agenda, exercise scripts, evaluation rubrics, and governance checkpoints matched to your vocabulary (banking, FMCG, engineering, etc.).

  4. 4

    Engaged, hands-on delivery

    Facilitation-led sessions with live exercises, breakout prompts, and documented failure modes—minimum passive lecture time.

  5. 5

    Post-session support: documentation & next steps

    Written recap, pilot backlog, links to explainx.ai courses for scaled upskilling, and optional office hours so momentum doesn’t stop at the workshop.

modules

Agent anatomy — planner, tools, memory, escalation

Avoid science-fair wiring diagrams in critical paths.

session outline

  • Boundaries: which tasks are tool-calling vs. pure generation.
  • Structured outputs and JSON schema discipline for downstream systems.
  • Idempotency and partial failure when calling internal APIs.

labs

  • Diagram a target workflow with rollback arrows annotated.

beyond-catalog topics (custom)

  • Sidecar vs. monolithic orchestrator tradeoffs common in regulated enterprises.
  • Runtime policy injection (e.g., per-department tool allowlists).

MCP in the enterprise: rollout & governance

So MCP servers don’t become shadow IT.

session outline

  • Publishing lifecycle for internal MCP servers; versioning expectations.
  • Logging: what must be retained for security reviews vs. what harms privacy if over-logged.

labs

  • Write an MCP server ‘definition of done’ checklist for your environment.

beyond-catalog topics (custom)

  • Secrets hygiene patterns when developer laptops connect to shared sandboxes.

quick contact

Scope or pilot this curriculum

Share sponsor, headcount, and cities — we reply with timing and options. Rough budget helps us match the right depth.

related on-demand courses

Agent Skills: Claude Code, Cursor and MCP in Practice

Ship Agent Skills, Claude Code Workflows, and MCP Integrations: Hands-on Training for SKILL.md Authoring, Cursor Productivity, and MCP Server Projects

Intro to MCP (Model Content Protocol)

Get Started with MCP: Understand Model Context Protocol Architecture, Build Your First MCP Server, and Connect Claude to External Tools and Data

Intro to AI Agents: Build an Army of Digital Workers with AI

Learn to Build, Deploy and Manage AI Agents: Practical Strategies for Automating Tasks, Streamlining Workflows, and Scaling with Digital AI Workers

Evaluating AI Agents

Learn to Evaluate AI Agents Rigorously: Benchmarking Accuracy, Reliability, and Safety with Automated Test Harnesses and Evaluation Frameworks

Intro to Google's A2A Protocol: Interoperable AI Agents

Learn Google's Agent-to-Agent (A2A) Protocol: Agent Interoperability, Combining A2A with MCP, and Building Multi-Agent Systems That Work Together

faq

Do we need engineers in the room?

Yes for half the modules—otherwise decisions drift into slide-level architecture.

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