The latest articles on DEV Community by Ajaykumar Yavagal (@akyavagal). https://dev.to/akyavagal https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3920419%2F9fbaaace-6101-455e-b824-2f6c253cec70.png https://dev.to/akyavagal en Ajaykumar Yavagal Fri, 08 May 2026 16:31:24 +0000 https://dev.to/akyavagal/prodseer-ai-powered-production-failure-prediction-3icc https://dev.to/akyavagal/prodseer-ai-powered-production-failure-prediction-3icc <h1> Building ProdSeer: AI-Powered Production Failure Prediction™ </h1> <p>Modern systems rarely fail because of one bug.</p> <p>They fail because of hidden operational complexity:</p> <ul> <li>cascading dependencies</li> <li>infrastructure bottlenecks</li> <li>observability blind spots</li> <li>external API failures</li> <li>scaling assumptions</li> </ul> <p>So for the MeDo Hackathon, I built <strong>ProdSeer</strong> — an AI-powered Production Failure Prediction™ platform.</p> <p>ProdSeer analyzes GitHub repositories, simulates cascading infrastructure failures, and forecasts production survivability before deployment using structured AI reasoning workflows.</p> <h2> What ProdSeer Does </h2> <p>ProdSeer goes beyond static code analysis.</p> <p>It:</p> <ul> <li>analyzes repository architecture</li> <li>infers production topology</li> <li>identifies operational bottlenecks</li> <li>simulates cascading failure scenarios</li> <li>forecasts survivability under production pressure</li> <li>generates infrastructure redesign recommendations</li> </ul> <h2> Some Features </h2> <p>⚡ Repository intelligence<br> 🕸️ Infrastructure topology visualization<br> 🔥 Cascading failure simulation<br> 📉 Survival probability forecasting<br> 🛡️ Operational risk analysis<br> 💬 Conversational infrastructure reasoning<br> 📄 Executive production-readiness reports</p> <h2> The Most Interesting Part </h2> <p>One of the wildest moments during development was watching AI reason about:</p> <ul> <li>AI-agent architectures</li> <li>degraded operational modes</li> <li>LLM dependency bottlenecks</li> <li>secure command execution</li> <li>infrastructure survivability</li> </ul> <p>The system began generating surprisingly believable operational redesigns involving:</p> <ul> <li>Kubernetes</li> <li>Redis</li> <li>observability pipelines</li> <li>secure execution sandboxes</li> <li>API resilience layers</li> </ul> <h2> Tech Stack </h2> <ul> <li>MeDo AI Framework</li> <li>Gemini 2.5 Flash</li> </ul> <h2> Final Thoughts </h2> <p>AI is making software development dramatically faster.</p> <p>But as AI-generated systems become more complex, operational uncertainty also increases.</p> <p>ProdSeer was an experiment in exploring what AI-native operational intelligence could look like.</p> <p>And honestly… watching AI simulate infrastructure collapse scenarios was mind-bending 😄</p> <h2> Links </h2> <p>🚀 Live App: <a href="https://app-bhgywf5bbuv5.appmedo.com/" rel="noopener noreferrer">https://app-bhgywf5bbuv5.appmedo.com/</a><br><br> 🎥 Demo Video: <a href="https://www.youtube.com/watch?v=y_sFMDpOYxs" rel="noopener noreferrer">https://www.youtube.com/watch?v=y_sFMDpOYxs</a> </p> <h1> BuiltWithMeDo </h1> ai lowcode cybersecurity