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Supercharge Workflows with AI Agents
Created by Shaunak Ghosh
Learn to move from chat-only AI to tool-using agents by connecting MCPs (tool connectors) that can read data, run searches, and take actions safely. You’ll identify high-ROI workflow bottlenecks, select trustworthy MCPs, set up your first MCP with minimal coding, and assemble a 3–5 MCP workflow chain you can run repeatedly and troubleshoot confidently.
Requirements
- Comfort using an AI chat assistant for work tasks
- Basic understanding of APIs, accounts, and permissions (no coding)
- Familiarity with common productivity tools (email, docs, calendars, trackers)
- Ability to copy/paste configuration snippets and follow setup checklists
What you'll learn
- Explain, in plain language, how agents differ from automations and why MCPs enable real tool use.
- Identify 2–3 high-ROI workflow bottlenecks and translate one into a well-scoped agent job with success criteria.
- Find, evaluate, and shortlist MCPs using trust, maintenance, permissions, and practical utility criteria.
Learning path
7 modules • Each builds on the previous one
1
AI agents and MCPs: clear mental model
Define AI agents vs MCPs in simple, accurate terms: agents plan and decide; MCPs are tool connectors that let agents read/write files, query systems, and fetch real data. Establish what MCPs can/can’t do so learners avoid expecting “magic” from a chat-only model.
2 videos9 min
2
Workflow pain points into agent jobs
Turn daily bottlenecks into well-scoped “agent jobs” (repeatable outcomes with clear inputs/outputs). Learn a quick audit method to pick tasks worth automating and define success metrics (time saved, fewer errors, faster decisions).
1 video5 min
3
Find and evaluate existing MCPs
Learn where to discover MCPs (official directories, GitHub repos, community lists) and how to evaluate them quickly: credibility, permissions, maintenance recency, documentation quality, and fit to your agent job.
1 video6 min
4
Set up your first MCP step-by-step
Do a guided, minimal-coding setup: connect an agent client to one MCP, grant only needed permissions, test a single action, and confirm outputs are real tool results (not hallucinations).
1 video6 min
5
Prompting patterns for reliable tool use
Use prompting patterns that make agents dependable with MCPs: clarify-first questions, tool selection rules, structured outputs, citations/logs, and “verify before conclude” behavior. Includes quick retrieval checks to diagnose why an agent chose the wrong tool.
2 videos9 min
6
Combine 3–5 MCPs into workflows
Design an end-to-end workflow chain: triggers, sequencing, handoffs, and error paths. Practice a common professional scenario (research → synthesize → draft → update project tracker → notify) while keeping each step observable and reversible.
4 videos19 min
7
Troubleshoot and choose custom solutions
Learn fast diagnostics for MCP workflows (auth errors, permissions, rate limits, tool mismatch) and decision criteria for when to use existing MCPs vs requesting custom integrations. Includes a simple “support ticket” template to ask for help efficiently.
2 videos10 min
Start Learning
Begin your learning journey
Modules7
Duration60 min
Science-backed learning
In-video quizzes and scaffolded content to maximize retention.
Key concepts
Agent Vs Automation Vs Tool Access Mental ModelsMCP Value Proposition And InteroperabilityWorkflow Audits And ROI-Based Automation Prioritization