Support Ops Agent

Inspiration

Leading technical teams to ensure system stability often highlights a major bottleneck: the manual overhead of post-incident reporting. After spending years on the frontlines of application support, I've seen firsthand how much bandwidth is drained by manually extracting metrics from ticketing systems to draft Root Cause Analyses (RCAs) and Problem Management reports. I wanted to build an agent that acts as a collaborative partner, automating this tedious busywork so support teams can focus on actual problem resolution.

What it does

The Support Ops Agent is an autonomous pipeline that ingests unstructured incident ticket data (like timelines, developer comments, and resolution notes). Using Gemini, the agent extracts key metrics, identifies underlying root causes, and automatically drafts comprehensive, enterprise-ready RCA documents. It also analyzes historical data to flag recurring patterns, assisting with proactive Problem Management.

How I built it

For this hackathon sprint, I focused strictly on the core agentic reasoning loop. I built a Python-based engine that processes mock ticket payloads (JSON) and interfaces with the Gemini API. I spent the majority of my time on prompt engineering and structured output generation to ensure the agent consistently produces standardized RCA formats (such as the 5 Whys, Incident Timelines, and Action Items) without hallucinating facts.

Challenges I ran into

Balancing a very tight development timeline with the need for high accuracy was tough. Specifically, fine-tuning the agent to accurately calculate metrics like 'Time to Detect' and 'Time to Resolve' from messy, unstructured human comments required several iterations of prompt refinement and testing.

Accomplishments that I'm proud of

I am incredibly proud of getting a fully autonomous reasoning loop working from end to end. Seeing the agent successfully parse a chaotic, multi-comment ticket thread and instantly output a clean, professional RCA document proved the massive real-world value of this concept.

What's next for Support Ops Agent

The immediate next step is moving from local JSON processing to live enterprise integrations. I plan to implement real-time webhooks for platforms like Jira Service Management and ServiceNow, and eventually deploy the agent on Google Cloud Run so it can instantly draft reports the moment a high-severity incident is closed.

Built With

• gemini
• google-cloud
• json
• prompt-engineering
• python
• vertex-ai