How to Build Your Learning Path Generator Agent

• Microsoft Copilot Studio
• AWS Bedrock
• Google Gemini Enterprise
• Salesforce Agentforce
• Cursor — the walkthrough below uses Cursor; Claude Desktop works the same way
• More platforms being added as they gain adoption

You're not locked in — the same underlying intelligence connects to whichever builder your organization runs on.

Step 2: Add a Data Source

Every agent builder needs to know where your customer data lives. There are two ways to connect it:

Step 3: Choose MCP Where Possible

CSV upload works, but MCP is the preferable path — it's the secure, standardized way for an agent to access live data, rather than a static file that goes stale the moment you export it.

Steps to Enable MCP on AI Platforms

Say you run a test-prep platform with 400 topics and 5,000 learners. You export the two spreadsheets you already have: your topic list with what depends on what, and the log of learner answers. Every time new answers arrive, without anyone rebuilding a study plan, the MCP updates every learner:

1. Your subject becomes a validated map. The agent checks your topics and prerequisites, and flags loops and missing links, so every learning order it suggests is actually possible.
2. Every learner gets a live mastery picture. Not a “completed” checkmark, but the probability that each skill is truly mastered, updated after every answer.
3. Our AI Agent finds the frontier a static syllabus would miss. Fractions is solid, Ratios is shaky, Algebra is locked. It knows exactly what this learner is ready for, and what they only think they’re ready for.
4. You ask our AI Agent a plain question. In Cursor, Copilot Studio, or wherever the agent lives, you type: “What should Aarav study next?” It queries the MCP in real time and answers: “Linear Equations. Fractions and Ratios are mastered; Linear Equations is the next unlocked topic.”
5. Our AI Agent explains itself when asked. You follow up: “Why not Quadratics?” It doesn’t deflect. It shows the why: Factoring is only 41% mastered, and it sits between Aarav and Quadratics.
6. Your team acts — this week, not next term. A path built by our AI Agent instead of a fixed chapter order is the difference between a learner who quietly quits at a topic they were never ready for, and one who gets the right lesson at the right moment.

That’s the shift: from a content library learners have to wade through, to a personal path for every learner — so you know exactly which lesson is worth their next hour.

“Here’s our topic list and answer log — what should each learner study next?”

“Which prerequisites are blocking Aarav from the Algebra exam, and how long will it take to fix them?”

“Show me the one prerequisite most of this batch is stuck on this week.”