Support Ticket AI Agent
A GenAI assistant that helps support teams resolve tickets faster through RAG, intelligent search and response drafting.
Business issue
Why I was brought into the project
Customer service teams needed to resolve support tickets faster while keeping responses accurate, consistent and grounded in existing technical knowledge.
Context
The environment around the project
Support teams spend significant time searching documentation and drafting responses for recurring issues.
Functional environment
The functional context covered individual ticket resolution, answer drafting, interactive troubleshooting and batch ticket processing.
Technical environment
The technical environment combined RAG, advanced agent capabilities, LLM reasoning, knowledge bases and integration with a ticketing system.
Challenges
The agent had to retrieve the right information, reason over ticket context, interact with ticketing workflows and produce answers that support teams could trust.
Solution
My contribution and its impact
My contribution to the project
I designed a GenAI agent connected to support knowledge and ticketing workflows, able to search relevant context, draft ready-to-use responses and assist technical teams through a chatbot mode.
- RAG-powered support ticket agent
- Ready-to-use response drafting flow
- Chatbot mode for technical troubleshooting
- Batch ticket processing capability
Impact
The solution reduced manual search and drafting effort, helping support teams respond faster while keeping more consistency across recurring issues.
- Reduced time spent searching for answers
- More consistent support responses
- Better support for both individual and batch ticket handling
Impact metrics
Approach
How I structured the work
- Map support ticket workflows and identify where AI could reduce manual effort.
- Connect the agent to knowledge sources and ticketing interactions.
- Design answer drafting and chatbot behaviors with traceable retrieved context.
Takeaways
What I learned from this project
- Support automation only works when retrieval quality and workflow integration are both strong.
- Human validation remains important when the agent produces customer-facing responses.
- A chatbot mode can extend the same agent architecture from ticket resolution to troubleshooting.