How a Platform Powered by Agentic AI for SDLC Automation Cut Costs by 50% for a Multinational Healthcare Enterprise

Key Highlights: What This Case Study Covers

• Development of a production-grade Agentic AI platform (“DEV AGENT”) to automate software development lifecycle (SDLC) workflows.
• Implementation of a scalable, multi-agent AI architecture on AWS, utilizing Amazon Bedrock for GenAI capabilities and LangGraph for orchestration.
• Strategies for reducing operational overhead and automating routine engineering and data pipeline tasks.
• Improvement of governance, data quality, and compliance standards within a highly regulated global healthcare environment.

Client Overview

Our client is a premier global healthcare, pharmaceutical, and medical technology enterprise headquartered in the United States, operating across more than 60 countries, with a global workforce of 10,000+ employees. They serve patients and healthcare providers worldwide under extensive regulatory, operational, and technological requirements.

The Ask

The core objective was to develop a production-grade Agentic AI platform (“DEV AGENT”) that could scale across the enterprise, reduce operational overhead, and establish a repeatable framework for AI-driven automation across future software development lifecycle (SDLC) use cases.

Challenges

• Routine activities required extensive manual intervention by the developers, which created high operational overhead.
• Data pipelines required automation and standardization to ensure reliability and consistency.
• Engineering efforts required realignment from operational maintenance to strategic innovation to enable effective scaling as data complexity increased.
• Compliance, auditability, and data quality required automated and standardized processes across global teams and regions.

Our Solution: DEV AGENT – Agentic AI for SDLC Automation

Built Multi-Agent AI Architecture

Developed a production-grade AI orchestration platform (“DEV AGENT”) using LangGraph and Amazon Bedrock, deployed on Amazon EKS to run autonomous software development workflows.

Integrated Conversational Interface

Connected the application directly into the client’s Bitbucket repositories, allowing engineering teams to manage codebases through a unified chat interface.

Automated Core SDLC Workflows

Enabled the system to natively perform automated impact assessments, standard code generation, and complex legacy migrations.

Synchronized Project Management

Linked the platform with Jira to automatically update user stories, track issue statuses, and handle Pull Request (PR) creation without manual overhead.

Established Enterprise Governance

Implemented Langfuse to provide full observability, deep performance tracking, and complete auditability across all autonomous agent decisions.

Impact Delivered

• Achieved an approximate 50% reduction in overall software development costs.
• Drastic reduction in manual developer hours by automating routine engineering and data pipeline tasks.
• Delivered a significant improvement in compliance, data quality, and operational consistency across highly regulated environments.
• Established a repeatable, enterprise-grade AWS/Agentic AI for SDLC automation framework ready to scale across future enterprise automation use cases.