Solution Architect

The system architecture is designed with five specialized AI agents (supervisor, knowledge base, graph database, machine learning, metrics monitoring) each handling distinct analytical domains to avoid processing conflicts.

A centralized supervisor agent implements intelligent query routing using chain-of-thought reasoning to determine optimal specialist selection and coordination strategies.

Agent-to-agent communication protocols enable parallel and sequential workflows with automatic topology data retrieval for ML predictions and real-time session management.

This modular approach ensures scalable query processing, eliminates agent overlap, and maintains consistent JSON-formatted responses with integrated visualization capabilities for complex enterprise infrastructure analysis.

Developed a GenAI-powered solution for automating method of procedure (MOP) document generation for live node upgrades (e.g., SDP, EMM).

Ingested historical upgrade documents into a RAG-based pipeline as a knowledge base.

Implemented a custom document parser to convert unstructured text into vector embeddings for context-aware retrieval.

Utilized GPT-4 to generate well-formatted, auto-structured MOP documents aligned with historical standards, significantly reducing manual effort and ensuring consistency.

Designed and deployed robust Langchain and Haystack-based pipelines on Kubernetes with support for multi-agent orchestration.

Enabled concurrent, multi-turn conversation workflows with contextual memory for scalable GenAI pipeline operations.