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AI Agents Mega Workshop Outline(Academy)

This document contains a brief outline of the AI Agents Mega Workshop we’re planning to conduct for Academy users in the month of May 2025.

Goal

Introduce students to the evolving world of AI agents, walk them through agent architecture, MCPs, use cases, and equip them to build and deploy production-ready agents—low-code or config-first.

Outline

1. Welcome & Context Setting

What are AI Agents? Why now?

Shift from SaaS → Autonomous Agents

Overview of what students will build today

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2. Understanding Agent Architecture

Objective: Deep dive into how agents think, act, and evolve

Current Limitations of LLMs: Assistants, not doers

Agentic AI Explained

From input to output: The agent decision loop

Key Architecture Blocks:

User Input Processing

Context Manager

Planner Module (Task breakdown)

Memory Block (Past conversations/use cases)

Tool Access Layer (APIs, browser, email, Salesforce, etc.)

Brain Block (Integration logic)

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3. Model Context Protocol (MCP) – The New API Layer

Objective: Teach students how to use MCP as a smarter interface for tools

Evolution from APIs to MCPs

Task formatting, permission handling, tool hierarchy

Why MCPs are more flexible and natural for agents

Tool integration examples: Slack, ClickUp, Salesforce

Architecture of a functioning MCP call

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4. Views from Visionaries

What Sam Altman, Andrej Karpathy, and others are saying

How companies are building MCP-compatible APIs

The market shift: SaaS fatigue vs Agent opportunity

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5. Use Cases of AI Agents

Objective: Explore real-world scenarios and inspire student ideas

Voice Agents: Sales calls, reservations, etc

Documentation Agents: Helpdesk clones

Coding Agents: AI pair programming via IDE plugins

Others: Research assistants, recruiters, business data analysts

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6. Building an AI Agent – Tools & Tech Stack

Objective: Help students choose the right stack for their needs

Low-code & Config-first options:

Crew AI, OpenAgents, etc.

Frameworks:

LangChain, Auto-GPT, Cursor

Frontends:

Streamlit, Gradio for UI

Deployment Tips:

Testing, rate limits (CPU/Lambda), API keys, safety

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7. Hands-On Project: Build Your First AI Agent

Objective: Guide students in building and deploying an agent end-to-end

Choose from 5–10 use cases (project templates provided)

e.g., Resume Reviewer, AI Market Researcher, QueryBot for docs

Steps:

Define agent config

Add memory, tools

Use MCP to access external APIs

Build frontend with Streamlit

Test → Debug → Deploy (local/cloud)

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8. Beyond the Build: Future & Scaling

Objective: Explore where students can take their learnings next

Opportunities with agents in careers & startups

How MCP is replacing SaaS for dynamic tooling

How many agents can you build? (Ideation brainstorm)

Challenges: Ethical use, hallucinations, throttling, etc.

Maintaining and improving deployed agents

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