Industry:
Energy
Location:
North America
Executive Summary:
AI-Driven Multi-Agent Knowledge and IT Support Solution for an Energy Industry Firm
A North American energy company sought to modernize its legacy knowledge and IT support chatbot, which was underperforming across key metrics. The existing system, built on static rules and scripts, delivered slow and often inaccurate responses, failing to meet the organization’s standards for employee engagement and operational efficiency.
To address this challenge, we proposed and designed a cloud-native, AI-powered multi-agent system hosted on Microsoft Azure. Built on the LangGraph orchestration framework and Azure AI Foundry. This solution integrates advanced ai agent hierarchies, allowing for contextual, domain-specific knowledge retrieval and automated IT support. It improves speed, accuracy, and adaptability, delivering measurable gains in support resolution time, employee satisfaction, and knowledge accessibility.
Business Use Case
Challenge:
The organization’s internal support chatbot was not scaling with the needs of its workforce. Employees experienced delays, poor response relevance, and limited capabilities in both research assistance and IT troubleshooting. This led to increased reliance on human support teams, raising operational costs and slowing productivity.
Solution Overview:
We implemented a LangGraph-based hierarchical multi-agent system, segmented by business domains (e.g., IT Support, Business Domain Knowledge).
It enables the creation of a multi-level hierarchical ai agent-based system by creating a top-level supervisor that manages multiple supervisor agents, each of which handle a business domain within the organization.
In this solution, each domain supervisor manages the worker or ReAct agents within its domain (IT support and Knowledge Retrieval).
Agentic Workflow:
Architecture:
Solution Components:
AI Agent Orchestration Framework: Langgraph Multiagent and multilevel hierarchies (Python)
Frontend: React.js, FastAPI, Chainlit Server (Dev), CopilotKit Agentic UI (Prod)
Memory Management/ Context Engineering: Azure Cosmos DB Memory Store
Data Source: Azure Data Lake Gen2
Vector Store: Azure AI Search Agentic Retrieval & Integrated Vectorization for Data ingestion, Query Decomposition and Parallel Subqueries
Secrets Management: Azure Key Vault
Traditional and AI Agentic Observability: Azure Foundry and Azure Monitor (Log Analytics and Application Insights)
Model Catalog: Azure AI Foundry
LLM-Judge Based Evaluation: Online Evaluation of GenAI App with Azure AI Evaluation Python SDK
Guardrails and AI Content Safety: AI Foundry Content Safety, Prompt Jailbreaks and Blocklists
AI Governance: Azure AI Foundry
Security: Managed Identity, RBAC, Network Security
Responsible AI
DevOps: GitHub Actions for Apps & Infra CICD, Azure App Service for hosting
Strategic Value:
This solution lays the groundwork for enterprise-wide AI adoption by creating a flexible, modular and extensible agentic framework. It not only replaces a legacy system but enables future expansion into HR, Compliance, and Operational domains with minimal overhead.
This is the first in a series of future posts will provide a deeper dive into specific components of the solution.
Microsoft