Ekaanksh Patil@ekaankshdev541

CivicFlow is an AI-powered citizen grievance automation platform designed to make complaint handling faster, smarter, and more transparent. Citizens can submit issues such as potholes, garbage collection delays, water leakage, and broken streetlights through a simple web interface. The system automatically validates each complaint, classifies it into the right category, assigns the correct department, generates a unique complaint ID, and tracks the full status timeline. The platform also includes a dedicated AI Assistant page where citizens can ask general civic questions or get complaint-specific help by entering a complaint ID. This makes the experience more interactive and useful, especially for users who want quick answers without navigating multiple pages. On the administration side, CivicFlow provides a separate admin dashboard for complaint management. Officials can view complaints, apply filters, update statuses, review duplicate reports, monitor SLA deadlines, and view analytics. The system includes auto-escalation logic so unresolved complaints are flagged automatically when they exceed the allowed time. It also includes an AI Escalation Agent that gives recommendation-based actions for at-risk complaints. CivicFlow improves the grievance process by reducing manual work, improving routing accuracy, ensuring better transparency, and helping citizens stay informed about the progress of their complaints. It is built as a practical, scalable civic-tech solution for smarter public grievance redressal.

AutoDebug is a multi-agent pipeline built entirely on Band that autonomously detects, analyses, and reports bugs in GitHub repositories — with a human always in the loop for the final fix. Five independent agents run as real Band-registered processes, coordinating through a shared Band chat room using @mentions. Each agent hands off accumulated context as JSON to the next, forming a clean pipeline: 1. DetectorAgent — clones the repo, finds failed CI runs via GitHub Actions API or runs Python files directly to surface errors 2. AnalyserAgent — uses AI to identify root cause, severity (low/medium/high/critical), and affected files 3. FixerAgent — generates a precise, minimal code fix with explanation using AI 4. TesterAgent — runs pytest to confirm the bug is real and reproducible 5. ResolverAgent — writes a clear, actionable fix report and delivers it by email The system never auto-merges code. Every fix is a report, not a commit — a human always reviews and applies the change. This human-in-the-loop design makes AutoDebug safe to run on production repositories. Built with Band SDK, NVIDIA API (MiniMax M2.7), GitHub API, Gmail SMTP, and pytest. Includes a CLI tool (autodebug start/stop/status/doctor) and a local fallback pipeline for offline testing.

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📅 June 12–19, 2026 • June 12: Kick-off Stream • June 12–19: Online Build Phase • June 19: Project Submissions Ends 🌍 Fully online hackathon. Join and build from anywhere in the world. 🤝 Build practical enterprise workflows where AI agents communicate, coordinate, exchange context, and complete work together. 🧠 Powered by Band and Codeband, built for collaborative AI systems and agent orchestration. 🏆 Prize Pool: $10,000+ 🧑‍💻 Apply now to build the next generation of enterprise multi-agent systems.