Source: unstop Remote / Online
Agentic AI Hackathon
Organized by Sahyadri College of Engineering & Management (SCEM), Mangaluru•university•North America
DEADLINEOct 5, 2026Registration Closed
EVENT DATESOct 5 - Nov 11Timeline window
PRIZE POOL🏆 Certificates & SwagVerified bounties
Agentic AI Hackathon
About the Opportunity
Build an autonomous AI agent that can plan, use tools, reason, and take actions toward a real-world objective—not just generate responses.Teams will choose one problem track and build a functional agent demonstrating a planning → tool-use → action workflow with minimal human intervention.Problem Tracks:Choose One:Claims Triage Agent – InsuranceClient Intake Agent – Legal & Professional ServicesOn-Call Incident Agent – EngineeringShopping Assist Agent – RetailPortfolio Health Monitor – Wealth & InvestingTeams May Adapt the Chosen Scenario Using Mock/sample Data. Real Customer or Financial Data Must Not Be Used.Registration:Team Size: 2–4 MembersOpen to College Students with A Valid College IdRegistration Fee: ₹400 per TeamEach Participant Can Be Part of Only One Team.️ Technology:Any Agent Framework, Ai Model, Programming Language, or Development Platform May Be Used, Including Lang Chain, Lang Graph, Crew Ai, Auto Gen, Open Ai Agents Sdk, Claude Agent Sdk, or Custom Solutions.At Least One External Tool/api Call must Be Integrated Into the Agent Workflow.Evaluation:Projects Will Be Judged On:Agentic Architecture – 30%Technical Execution – 25%Problem Fit & Impact – 20%Innovation – 15%Presentation – 10%Event Details:Date: 14 November 2026Venue: CSE Central Lab Ground Floor Main Building, Sahyadri College of Engineering and Management, MangaloreLive Demo: 5 Minutes + 3 Minutes Q&aPrize Pool: ₹15,000RulebookThis Page Contains the Key Event Information. Participants Are Advised to Read the Official Rulebook for Complete Technical Requirements, Submission Guidelines, Track Details, Judging Criteria, Rules, and Code of Conduct.View Rule BookContact:Naren S J: +91 82968 33381Tharun Rai: +91 63608 29990PLAN. BUILD. ACT. AUTOMATE.
Relevant Tech & Domains
AI / Machine Learning
Score Composition (0–100)
Hiring Potential (25%)30%
Organizer Prestige (20%)85%
Accessibility & Format (15%)100%
Data Quality (10%)85%