SLM based Agentic Ticketing Solution

Overview:

SLM based Agentic Ticketing Solution is an AWS-powered agentic solution designed to modernize and simplify how organizations manage customer support tickets. It acts as a virtual layer for frontline support operations, capable of handling routine tasks like ticket triaging, standard operating procedure (SOP) execution, and identifying known or recurring issues—all without human intervention.

The solution uses intelligent agents and domain-specific language models that mimic the decision-making of experienced support professionals. These agents work together to understand the context of each issue, apply the right resolution path, and escalate only when necessary. This results in faster ticket resolution, fewer manual touchpoints, and a more consistent support experience for end users.

Currently, implemented using CrewAI for agent orchestration and Amazon SageMaker for fine-tuning and deploying verticalized Small Language Models (SLMs). The architecture is flexible—alternative implementations using Amazon Bedrock Agents are also possible, offering a fully managed, serverless approach to building and scaling agentic workflows within the AWS ecosystem.

Key Features:

1. Agentic Framework: The solution uses a multi-agent setup powered by CrewAI, where each agent specializes in a specific support function such as ticket triage, network diagnostics, or root cause analysis. This modular design allows for expert-level automation. This framework could be adapted to use Amazon Bedrock Agents, which would provide native orchestration, grounding, and tool-calling capabilities within the AWS ecosystem—offering tighter integration and managed scalability.
2. SLM-Driven Intelligence: Verticalized Small Language Models are fine-tuned and deployed using Amazon SageMaker. This enables the solution to deliver accurate, domain-specific responses while maintaining a lightweight and cost-efficient model footprint.
3. Retrieval-Augmented Generation (RAG): A backend RAG pipeline powered by OpenSearch retrieves relevant context to enhance the quality of agent responses, ensuring decisions are grounded in historical and real-time data.
4. Lightweight, Scalable Infrastructure: The solution is hosted on an Amazon EC2 instance, providing a flexible and scalable environment that supports rapid deployment and easy maintenance.
5. Seamless System Integration: The solution connects with ITSM platforms, NOC systems, and enterprise databases, enabling a unified and automated support experience without disrupting existing workflows.
6. Reflection Agents for Continuous Learning: AI-based reflection agents learn from past resolutions and feedback loops, improving accuracy and reducing resolution time over time.

Benefits:

1. Faster Customer Response Times: Customers receive quicker resolutions as the solution automates the initial triage and resolution steps, reducing wait times and improving satisfaction.
2. Lower AI infrastructure costs due to SLMs: By using lightweight, domain-specific Small Language Models instead of large general-purpose models, the solution delivers high accuracy at a fraction of the compute cost—translating to direct savings for customers.
3. Lower Operational Costs: By automating L1 support tasks, organizations can reduce the need for large support teams, leading to significant cost savings over time.
4. Improved Service Consistency: the solution ensures that every ticket is handled with the same logic and precision, minimizing human error and variability in support quality.
5. Scalable Support Without Scaling Headcount: As ticket volumes grow, the solution can handle increased load without requiring proportional increases in support staff.