AgentGuard: Resilient AI Ops

Inspiration What it does How we built it Challenges we ran into## Inspiration AI agents are becoming part of real applications, but they often fail in messy real-world conditions: LLM providers time out, APIs hit rate limits, tools crash, and users are left with no clear explanation. AgentGuard is a small reliability dashboard for AI agents. It helps developers simulate failures, retry safely, route to fallback providers, and understand what happened through clear logs and metrics. What it does AgentGuard lets a developer run an AI task and observe how the system behaves under failure. The app can simulate timeout, rate limit, model failure, and tool failure scenarios. When something fails, AgentGuard records the event, retries when appropriate, switches to a fallback path, and displays reliability metrics such as success rate, retry count, fallback usage, and average latency. How we built it We built the project as a web app with a simple agent execution flow, a failure simulation layer, fallback routing, event logging, and a dashboard for reliability metrics. Challenges we ran into The main challenge was making failure states easy to understand. Instead of hiding errors, the app turns them into visible events so developers can see what happened and why the fallback path was used. Accomplishments We created a focused demo that shows the full lifecycle of an AI agent request: normal execution, failure detection, retry, fallback routing, and reliability reporting. What's next Next we want to connect AgentGuard to real production agent frameworks, support more providers, and add alerts for repeated failures. Accomplishments that we're proud of What we learned What's next for AgentGuard: Resilient AI Ops