Enhancing real-time business impact through dynamic, decision-driven workflows with LLM-powered agents

Overview

This case study highlights how generative AI agents, integrated within Databricks, enable dynamic decision-making and efficient task automation. By leveraging large language models (LLMs) such as LLaMA-3 and OpenAI, the system intelligently perceives inputs, reasons through tasks, and takes context-driven actions. The agentic approach transformed traditional rule-based systems into adaptive, multi-step decision frameworks, significantly enhancing efficiency and user engagement.

Objectives

Demonstrate intelligent, reasoning-based agent design within Databricks.
Compare non-agentic vs. agentic systems, highlighting dynamic decision-making capabilities.
Showcase a scalable architecture for automated decision support using LLMs.
Enable real-time business impact by transforming cost/log data into insights.

About Client

An American company specializing in generative AI solutions, leveraging advanced data processing and language models to automate decision support and enhance user interactions. The company’s scalable solutions drive intelligent decision-making in various domains, including data analytics, customer support, and content generation.

Industry

Gen AI

Years in Business

15 Years

Company Size

200 Employees

Geographical Presence

California, USA

Strategy: A Thoughtful Path to Success

In strategizing auto manufacturer's infrastructure, Info Services conducted a thorough assessment of TADs, proposed strategic recommendations aligning with industry best practices, executed comprehensive implementation with Terraform, and fostered collaborative tracking with key stakeholders.

Comprehensive Assessment

Conducted a comprehensive evaluation of approved Technical Architecture Documents (TADs) to understand Azure services, security, and governance policies.

Strategic Recommendations

Proposed an architectural viewpoint, leveraging industry best practices, and collaborated closely with Microsoft and Databricks teams.

Thorough Implementation

Executed approximately 50 Terraform base modules on Azure, created environment-agnostic stacks, and orchestrated them with Jenkins and Terragrunt.

Collaborative Tracking

Worked closely with the client architects, network & security teams, Microsoft, and Databricks to ensure a highly secure and resilient infrastructure provisioned with necessary governance guardrails.

Challenges

Traditional systems lacked contextual understanding and dynamic task execution, resulting in static outcomes and limited adaptability.
Integrating multiple tools for complex queries posed significant challenges.
Ensuring version control, security, and reproducibility of deployed models remained problematic.
Addressing these challenges required an agentic architecture capable of reasoning and leveraging diverse data sources dynamically.

Solutions

The company designed a modular, LLM-powered Agentic System within Databricks. Key components included:

Modular Agentic System: Built using LLMs (such as LLaMA-3 or OpenAI) within the Databricks environment.

Architecture Components:

Delta Lake: Manages structured data efficiently.
PySpark DataFrames: Facilitates data preparation and transformation.
Agent Tool Functions: Provides modular task execution.
Prompt Templates: Mapped to tools for efficient use.
MLflow + Unity Catalog: Ensures governance and version control.
Databricks Model Serving: Supports real-time deployment.

User Interaction:

Allows querying agents via API or UI.
Supports natural language interaction and dynamic execution.

By allowing natural language queries via API or UI, the system facilitated dynamic interactions and decision-making, enhancing workflow automation.

Technologies Used

Summary

By leveraging Databricks and advanced LLMs, the client transformed their decision support system from static rule-based workflows to dynamic, reasoning-based automation. This innovative architecture enhanced efficiency, enabling real-time insights and scalable deployment across the organization.

Impact

Transformed rigid workflows into adaptive, decision-driven processes

Enabled natural language querying of structured data, improving user experience

Enhanced response quality using zero-shot and few-shot prompting

Created a reusable, governed AI agent framework

Achieved scalability and real-time execution through API and chatbot interfaces