UpTime.ai

Inspiration

Developers move fast, and production errors can appear at the worst times. We wanted to build an AI-powered system that acts like an on-call engineer by watching server issues, judging severity, and helping teams respond faster.

What it does

Uptime.ai is a real-time intelligent dashboard that monitors server errors through Sentry and uses Fetch.ai agents to decide whether an issue needs deeper investigation. It helps developers quickly understand what went wrong, how serious it is, and what should happen next.

How we built it

We built Uptime.ai using Sentry for error monitoring and Fetch.ai for the multi-agent workflow. When Sentry detects an issue, it sends the event to our backend and dashboard via webhook. From there, our Fetch.ai agent chain analyzes the error, determines its urgency, and prepares the next steps for review.

Challenges we ran into

*Our biggest challenge was designing the agent judgment process. Not every error should trigger the full pipeline, so we had to make the agents reason about severity, context, and whether the issue was worth escalating. *A big problem was creating the infrastructure to bridge two applications in real time. We wanted to host a publicly accessible VM for a demo, but we couldn't get port forwarding to work on the guest wifi. *Sentry was sometimes inconsistent on webhooks. We had to configure quite a few settings to get it to work well.

Accomplishments that we're proud of

Built a multi-agent judgment pipeline using Fetch.ai.
Integrated Sentry for real-time error and server health monitoring.
Connected the dashboard to a GitHub repository so agents can use project context.
Created the foundation for an AI-powered on-call engineering assistant.

What we learned

We learned how to connect monitoring tools with agentic AI systems, and how difficult it is to make agents reason carefully instead of blindly reacting to every error. We also learned more about building reliable workflows for real-world developer tooling.