Amna Masood@Amna_11
1
Events attended
1
Submissions made
Pakistan
1 year of experience
About me
Hi! currently, I'm doing my degree in Psychology with a growing interest in artificial intelligence, technology, leadership, and social impact. Previously, alongside my studies, I have earned a diploma in Robotics. I am passionate about using technology and innovative thinking to address community challenges. While I am still developing my technical and professional experience, I am committed to continuous learning, collaboration, and creating solutions that make a positive impact. Through AI hackathons and interdisciplinary projects, I look forward to learning from others, contributing fresh perspectives, and exploring how technology can be used to empower individuals and communities.
🤝 Top Collaborators
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🤓 Latest Submissions
CrisisNet — Multi-Agent Crisis Coordination
CrisisNet is a multi-agent AI system built for food and energy security decision support. When a crisis hits — drought, energy spike, supply chain collapse, or market shock — analysts across agriculture, logistics, energy, and finance each build their own picture separately. By the time the reports reach a decision-maker, the window for early intervention has closed. CrisisNet solves this by deploying five specialised AI agents simultaneously: a Farmer agent analysing crop yields and climate risk, a Logistics agent mapping supply routes and bottlenecks, an Energy agent tracking grid load and fuel prices, a Market agent monitoring demand signals and speculation, and a Regulator agent that synthesises all four into a final policy recommendation. Each agent queries a domain-specific vector knowledge base (ChromaDB RAG) built from real FAO reports, USDA stock data, and regional agricultural statistics before calling an open-source LLM via Featherless AI. Agents coordinate through Band AI's platform — every finding is published to a shared Band Room with a full audit trail. A coordinator automatically detects conflicts between agent signals — for example, when market price recommendations contradict supply shortage data. The Regulator agent then issues a structured policy decision: emergency imports, reserve releases, subsidy plans, price controls — with a calibrated confidence score. If confidence falls below 70% or critical thresholds are breached, the system escalates to a human with a clear explanation. The output is a professional PDF report ready to share with a minister — crop forecasts by region, logistics route plans with costs, price recommendations per commodity, and subsidy proposals with budget estimates. Built on FastAPI, Band AI, Featherless AI, ChromaDB, and React.
19 Jun 2026
