Harsh Verma@vharsh2615709
2
Events attended
1
Submissions made
India
1 year of experience
About me
I’m Harsh, a hackathon project leader and AI/ML enthusiast passionate about building innovative solutions with generative AI and cloud technologies. With a PGP in Artificial Intelligence & Machine Learning from UT Austin and an upcoming Master’s in Data Science at Deakin University, I combine academic depth with hands‑on hackathon experience. As leader of the Sūtradhāra team in Gen AI Academy APAC, I’ve driven projects from concept to demo, focusing on workflow optimization, creative branding, and algorithmic trading. My goal is to design credible, submission‑ready AI applications that balance innovation with compliance and privacy.
🤝 Top Collaborators
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🤓 Latest Submissions
Nadi AI: Autonomous Multi-Agent Response Pipeline
Our project introduces a fully automated, multi-agent incident response system designed to reduce software downtime from hours to seconds. By leveraging the Band SDK for seamless agent-to-agent communication, the system operates as a continuous pipeline that detects, diagnoses, and resolves runtime errors without manual intervention. The Architecture: Agent 1: The Sentry (Diagnostics & Triaging): Acting as the first line of defense, this agent intercepts raw, unstructured crash logs. Using LangChain and Qwen2.5-Coder, it deterministically parses messy logs into strict, Pydantic-validated JSON payloads (extracting error type, severity, and stack traces) and securely publishes them to a designated Band Room. Agent 2: The Patch Engineer (Resolution): Operating asynchronously, this agent monitors the Band Room for new Sentry reports. Upon receiving a structured error payload, it utilizes Mistral (via the Featherless AI API) to analyze the fault and generate a targeted, executable code patch. Agent 3: Command Center & Reviewer (Oversight): The entire ecosystem is visualized through a centralized Streamlit dashboard. Before any code is pushed, a CrewAI-powered Reviewer Agent evaluates the Patch Engineer's proposed fix for security and structural integrity, ensuring human-in-the-loop visibility and control. Key Technologies: Band SDK, LangChain, Qwen2.5-Coder, Mistral (Featherless AI), CrewAI, Streamlit, and Pydantic. Impact: By combining structured LLM outputs with multi-agent networking, we have created a scalable, intelligent DevOps automation tool that treats runtime errors not as blockers, but as instantly solvable data events.
19 Jun 2026
