Overview Julie is an AI-powered exam preparation assistant designed to help students prepare smarter and faster for competitive exams using a multi-agent, agentic AI architecture. Primary Objectives • Build a functional agentic AI exam preparation system • Demonstrate end-to-end agent orchestration • Deliver a demo-ready, runnable system • Evaluate candidates for long-term collaboration Minimum Scope (Must Deliver) • Multi-agent system (Tutor + Mock Test + Web Search) • Agent orchestration and coordination logic • Mock test generation with scoring • Runnable codebase (local or cloud) • Stable end-to-end demo flow Deliverables & Submission Requirements • Demo video (2–3 minutes) showing the agent running end-to-end • Complete code submission (GitHub repository or ZIP file) • Code must run successfully during evaluation • Clear setup instructions (README.md) • Architecture overview (Notion, PDF, or GitHub README) • Output samples demonstrating explanations, mock tests, and results Rewards • 30,000 total Credit Joules reward pool • Reward amount will be equally divided among all team members • 2 selected candidates will receive a paid internship in the Agentic AI team • Opportunity to co-develop production-grade autonomous AI systems Reward – Terms & Conditions • Rewards will be released only after successful verification of all deliverables. • Non-runnable or broken code submissions will be disqualified. • Incomplete submissions are not eligible for rewards. • Final selection is subject to internal technical and product review. • Rewards will be paid within 7–14 working days after final approval. • Any plagiarism or license violations will result in disqualification. • All intellectual property rights of accepted submissions will belong to the organizing entity unless agreed otherwise in writing. Expected Outcome This bounty will help identify strong agentic AI engineers capable of building and explaining production-grade autonomous AI systems, while delivering a scalable foundation of Julie.