Sifting through numerous university web pages, portals, and disconnected databases to enroll in new courses, review appropriate electives, or check prerequisites, can result in student confusion and mistakes. At the same time, course advisors must respond to repetitive inquiries from hundreds of students about things like unit eligibility, course structure, or administrative procedures, and this consumes time and prevents advisors from focusing on high-value academic guidance and student well-being.

Team UniTED.AI’s solution, the "AI Academic Assistant," leverages IBM watsonx Orchestrate to build a collaborative network of agentic AI services designed to simplify information access, reduce human workload, and improve the student experience. Students can enter a natural-language request, such as, "Show me electives that fit my degree plan and prerequisites." Watsonx Orchestrate dynamically routes the request to the appropriate agents that gather and reconcile data from existing university portals to delivers a clear, personalized answer. Course advisors can also use the system to review and validate recommendations, ensuring visibility and oversight. The result is a smooth, AI-driven workflow that minimizes manual intervention while maintaining human supervision where necessary.

The solution uses multiple specialized AI agents: One to retrieve and summarize unit details, prerequisites, and credit requirements, one to aggregate common information about university services, including timetables, library resources, and campus facilities, and one to integrate student-specific data to provide tailored recommendations for electives or study plans. Early tests achieved 90% accuracy and 88% reliability when responding to real student queries.

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In moments of urban crisis such as infrastructure failures, flooding, fires, or road accidents, delays in coordinating the appropriate response can be critical. In rapidly growing urban areas like Bangalore, a few lost minutes can cost lives and property. Current public emergency systems rely heavily on manual reporting, which makes it challenging to assess, prioritize, and route help to incidents in real time.

The "Alert X" solution from team Agents FDRK seeks to make cities safer, more connected, and resilient. It helps reduces the gap between incident detection and emergency action from minutes to seconds. It’s is an autonomous, multi-agent AI framework that detects, analyzes, and communicates critical incidents to authorities with intelligent coordination. Built on IBM watsonx.ai and IBM watsonx Orchestrate, Alert X integrates visual understanding, contextual reasoning, and natural interaction into one continuous workflow. One agent captures live video frames from a mobile camera and uses Granite Vision or Llama-based visual models to identify the scene (e.g., flooded streets, fire, damaged infrastructure). It then produces a natural-language summary with detected risks and objects. Another agent uses Openstreet Map APIs to retrieve information on nearby critical facilities such as hospitals, schools, crowded spots, connectivity hubs, police stations, fire stations, and shelters. A third agent uses Granite 3.3 Reasoning LLM to analyze both the visual and the geographical data. It evaluates severity, impact, and urgency, then recommends actionable steps such as "Evacuate nearby area," "Ensure no children in the surrounding schools go outside," "Notify fire department," or "Divert vehicles to alternate route." Finally, after user approval, a fourth agent generates a human-like conversational script to brief authorities. It mimics a real person’s tone providing event context, urgency, and help needed, ensuring clear, empathetic communication.

Watsonx Orchestrate runtime triggers each agent, monitors outputs, and maintains the contextual flow. The team hopes to implement more agents in the future such as a drone data assessor or citizen chatbot.