Khel.ai 15 Day AI Agent Internship Program

Co-powered by SAAI & YouVah

Program Cost: INR 7999.00 (includes GST)

SAAI × Khel AI — 15-Day AI Agent Internship Roadmap

SAAI × Khel AI • AI Agent Engineering Internship

15-Day AI Agent Internship Roadmap

A student-friendly, product-first roadmap where learners build a real sports AI agent using FastAPI, analytics APIs, chatbot intelligence, memory, and tool calling.

Audience

9th–12th

School students learning real AI systems through guided product work.

Duration

15 Days

Fast, practical, visible progress with build-test-review cycles.

Project

Khel AI

Students build on top of the cricket analytics MVP.

Framework

Design → Develop → Deploy → Defend.

Core Mental Model

What students should understand before coding

The stylesheet is scoped around a structured infographic layout, so this page is now mapped to the exact class system it expects. The first teaching goal is clarity: an AI agent is not just a chatbot. fileciteturn0file0L1-L20

AI Agent Formula

Core definition

AI Agent = Chatbot/System + LLM + Memory + Tools + Decision Layer

Students should understand that the system must talk, remember, and act.

System Flow

1
User asks a question
2
Chatbot receives it
3
LLM understands the intent
4
System decides whether a tool/API is needed
5
Tool/API returns data
6
Useful context goes into memory
7
Agent returns a grounded answer

Program Outcomes

What students will build by Day 15

FastAPI

Backend APIs

Students build real sports analytics endpoints that can be tested and reused.

Chatbot

LLM Layer

A conversational layer that understands user intent and responds clearly.

Memory

Context Recall

A simple memory store so the system can remember previous context.

Agent Mode

Tool Calling

The system decides which API to call instead of guessing the answer.

Visual Schedule

15-day Gantt view of the internship flow

This chart helps students see how learning moves from foundation to APIs to intelligence to final demo.

Phase 1

Students first understand the system before building it.

Phase 2

Three API sprints create visible backend progress.

Phase 3

Best APIs are selected for reuse in the MVP.

Phase 4

The system becomes an agent once tools and memory are wired in.

Day-wise Roadmap

The 15-day internship path

Each phase is designed to keep momentum high and outcomes visible for younger learners.

Days 1–3 Foundation

How intelligence systems work

Students learn what an AI agent is, how it differs from a chatbot, what an API is, and how sports AI systems are structured.

Day 1

AI agent architecture, sports AI flow, and system components.

Day 2

FastAPI basics, endpoints, requests, responses, and Swagger.

Day 3

First cricket score API and introduction to validation thinking.

Days 4–5 Sprint 1

Scoring APIs

Students build scoring endpoints and then test and defend them.

Current score Run rate Required run rate Last over summary Batsman stats Bowler stats

Days 6–7 Sprint 2

Player Analytics APIs

Students move from basic scoring to deeper player-level analytics.

Strike rate Economy Boundary % Dot ball % Wicket probability

Days 8–9 Sprint 3

Match + Tournament Analytics APIs

Students begin building system-level intelligence for match and tournament insights.

Team performance Win probability Points table Net run rate Leaderboards Top performers

Day 10 Selection

Review + Integration Day

The best APIs from the cohort are selected and prepared for integration into the Khel AI MVP.

Accuracy

30%

Clean Code

20%

API Docs

15%

Testing + Edge Cases

35%

Days 11–12 Intelligence Layer

Chatbot + LLM + Memory

Day 11

Build the chatbot layer and understand prompt design.

Day 12

Create a simple memory system using Python structures.

Memory is taught simply: if the user asks a follow-up question, the system should not behave like it forgot the previous step.

Days 13–14 Agent Mode

Tool Calling

This is where students realize that an AI agent should fetch data instead of making unsupported guesses.

Tool-calling flow

User asks a question
↓
LLM checks intent
↓
System chooses a tool/API
↓
API returns data
↓
Agent responds with grounded output

Day 15 Demo Day

Final Demo + Defense

✓
Show the architecture
✓
Explain the APIs built
✓
Demonstrate chatbot flow
✓
Explain memory design
✓
Show tool calling in action
✓
Defend why the system was built that way

Internship Structure

How to run it like a real product team

Build Day

Create

Students build the assigned API or agent component with a focus on correctness and naming.

Test Day

Validate

Students test with Swagger, invalid inputs, and edge cases before claiming success.

Review

Select

The cohort compares implementations and selects the strongest APIs for integration.

Final Output

What the cohort presents at the end

Capstone

Khel AI Junior Agent

A working sports intelligence assistant that can answer cricket questions, remember context, and call FastAPI endpoints to produce grounded responses.

System Part

APIs

Analytics services built by students.

System Part

LLM

Prompted reasoning layer.

System Part

Memory

Context continuity for follow-up questions.

System Part

Tools

Tool calling and orchestration.

This internship does not just teach coding. It teaches how real AI products are designed, built, tested, integrated, and defended.

Buy Now

Free Preview Lessons

Video coming soon

Architecture & Fast API Basics

Day 1 — AI Agent Architecture, Sport AI Flow, System Components

Day 1 — AI Agent Architecture, Sport AI Flow, System Components /* ========================================================= SAAI INFOGRAPHIC STYLESHEET — SCOPED VERSION Safe for Django base.html with share…

Explore Modules

Architecture & Fast API Basics

Day 2 — FastAPI Basics for Khel AI MVP

SAAI Day 2 — FastAPI Basics for Khel AI MVP SAAI • Day 2 • FastAPI + Sports Data FastAPI Basics for Khel AI MVP Learn APIs like a class 10 student, but …

Explore Modules

Architecture & Fast API Basics

Day-3 Deploying Fast API

SAAI Day 3 — Deploying FastAPI on Render SAAI Internship Program · Powered by Khel AI Day 3 — Deploying FastAPI on Render In this session, students will take a loca…

Explore Modules

Sprint 1: Scoring API

Day 4 Khel AI MVP — Data Models to API Design

Khel AI MVP — Data Models to API Design SAAI • Khel AI MVP • Backend Foundations Khel AI MVP — Data Models to API Design Today’s session helps students …

Download Notes Explore Modules

Video coming soon

Sprint 1: Scoring API

Day 5: Improvements and Integrations

SAAI — From Standalone API to Integration-Ready API SAAI • Khel AI MVP • API Engineering Lesson From Standalone API to Integration-Ready API This lesson…

Explore Modules