Modernizing legacy architectures using GenAI-powered Knowledge Graphs
Reading Time: 5 minutes
A decade ago, enterprises started began migrating their data platforms and associated applications to the cloud, but most stopped at lift-and-shift, with legacy systems just rehosted and not reengineered. Today, these data platforms such as legacy data warehouses or data lakes are complex, costly, and unfit for recent AI-native business demands.
Legacy platforms often require massive manual effort to restructure and optimize. However, According to McKinsey, GenAI tools have enabled software engineers to complete coding tasks up to twice as fast, reducing the time needed for documentation and pipeline development by 50%. GenAI is helping businesses have to unlock value from data, accelerate insight, and compete at the speed of innovation.
Why Traditional Modernization Falls Short
Most “lift and shift” migrations rehost legacy systems without re-architecting them,creating platforms that are structurally unfit for modern AI workloads. In the Agentic AI era, where systems must reason, act autonomously, and iterate in real-time, these rigid architectures become bottlenecks. The deeper issues often lie in fragmented processes, undocumented logic, siloed teams, and deep entanglement with critical business workflows. This leads to unclear ROI, stalled adoption, and a widening gap between data capabilities and business outcomes.
Engineering teams face recurring questions:
- How can we understand legacy systems when documentation is missing and domain knowledge is lost?
- Can we preserve business logic while translating code across tech stacks?
- How do we minimize disruption while ensuring the new system maintains functional parity and delivers the same outcomes?
These challenges call for a smarter approach, one that’s automated, insight-driven, and GenAI-powered.
Enabling structural visibility with Knowledge Graphs
Modernizing legacy systems or building AI copilots for developers requires deep visibility into how applications are architected, not just line-by-line, but at the system level. One of the most effective ways to enable this visibility is by converting the codebase into a knowledge graph that exposes entities, relationships, and behavioral semantics. Let’s explore how GenAI transforms legacy systems through a knowledge-graph-driven approach.
From Codebase to Knowledge Graph
GenAI-assisted approach to legacy migration is powered by advanced technologies—including context engines, prompt engineering, foundation models, and self-validation loops—where automation and GenAI take the lead. Here’s how that transformation happens in practice:
- Challenge: Conventional rule-based data quality checks often miss context-specific issues or fail to adapt to schema changes in data. Lineage tracking, too, tends to be outdated or incomplete, limiting visibility and traceability across pipelines.
- Value add: Agentic AI applications monitor for anomalies, interpret schema shifts, and automatically update lineage maps. These agents learn from feedback and improve accuracy over time, significantly reducing the manual burden of maintaining data integrity.
- Implementation example: A leading alcohol beverage manufacturer partnered with Sigmoid to deploy agentic AI for automating data quality checks and lineage tracking. Specialized agents validated file schemas, flagged anomalies, summarized risks, and generated actionable recommendations. By continuously learning from feedback, the system improved validation accuracy over time while maintaining traceable metadata across stages. This significantly reduced manual intervention with auditable data pipelines across the organization.
2. Data discovery
- Challenge: As data ecosystems grow, the number of datasets and reports multiply, often without a unified system to manage them. This leads to duplication, poor discoverability, and inefficient data use.
- Value add: Enterprise AI agents streamline data product and platform management by enabling metadata enrichment, user role-based access, and intelligent search. These agents can automatically categorize datasets, infer data usage patterns, and surface relevant insights through conversational interfaces.
- Implementation: A global consumer health company partnered with Sigmoid to modernize its enterprise data platform. Intelligent agents were used to enhance metadata enrichment, enable intuitive data discovery, and reduce duplication across datasets. The platform also integrated reporting and catalog tools for seamless access. This agent-led approach improved data reuse, streamlined onboarding, and delivered a more efficient user experience across business functions.
3. DataOps and observability
- Challenge: In fast-moving data environments, even minor upstream changes can cascade into downstream pipeline failures. Traditional DataOps tools, often limited by rule-based triggers, fail to catch these issues early or provide actionable diagnostics.
- Value-add: AI agents for business introduces self-healing agents that monitor pipelines in real time, detect anomalies, and take corrective action—whether by restarting jobs, switching to backup sources, or updating transformation logic. Observability agents also provide detailed diagnostics, helping teams resolve complex failures faster.
- Implementation: A leading infant nutrition brand partnered with Sigmoid to modernize data operations using Agentic AI. Agents were deployed to monitor system health, classify issues by severity, and trigger automated resolution workflows. These agents proactively maintained uptime, reduced operational noise, and improved observability across complex data environments. The solution led to 70% faster issue detection, improved reliability, and a scalable model that significantly reduced manual intervention and overhead costs by 30%.
4. Master Data Management
- Challenge: Master Data Management (MDM) has traditionally relied on rule-based matching and manual oversight to maintain accurate, consistent, and unified records across systems. However, as data volumes grow and source systems become more diverse, these legacy approaches struggle to scale or adapt.
- Value-add: AI agents for business automate key MDM functions, such as detecting duplicates, resolving entity mismatches, and maintaining unified records across domains with minimal human intervention. LLM-powered agents can parse structured and unstructured inputs, understand semantic similarities, and continuously refine matching logic using feedback loops, resulting in high-accuracy master data at scale.
- Implementation: A global healthcare and life sciences company partnered with Sigmoid to automate master data creation from inspection documents and drawings. GenAI-powered agents extracted structured attributes from unstructured PDFs, standardized entries, and mapped them to LIMS-compatible formats. This reduced manual effort, accelerated validation, and improved consistency in golden records across systems, while laying the foundation for scalable, AI-driven master data management across labs and sites.
Operationalizing intelligent agents with AgentOps
As enterprises begin to embed AI agents across data engineering workflows, the need for AgentOps becomes critical. It is essentially the discipline of operationalizing and managing agentic systems scalably. Unlike traditional automation or standalone LLM deployments, agent-based architectures involve multi-step planning, memory retention, feedback loops, and tool orchestration.
AgentOps provides the framework to govern how agents are orchestrated, monitored, secured, and continuously improved. It ensures these systems align with business goals, perform reliably, and adapt over time without unintended behavior. This shift introduces new responsibilities for data and platform teams, from managing agent interactions and task decomposition to enforcing guardrails and maintaining observability.
Adoption of agents across data workflows
The shift toward agentic AI in data engineering is gaining momentum as major cloud providers are embedding GenAI into their data workflows. Capabilities like natural language-to-ETL conversion are becoming standard, allowing engineers to write pipeline code, detect schema mismatches, and recommend transformations—all through natural language prompts. This evolution is also changing how to build AI agents, with prompts now engineered with the same precision as SQL scripts. The result is lower technical barriers, faster development cycles, and more intuitive interaction with data systems.
At Sigmoid, we are actively deploying agent-based frameworks across the data engineering workflows. Early pilots have shown up to 30% reduction in time spent on tasks like data ingestion, pipeline creation, and operational oversight. With deep expertise in GenAI, LLM orchestration, and agentic AI services, we are helping our clients build intelligent, scalable architectures where every data engineer is paired with an AI-powered collaborator, working smarter, and with greater agility than ever before.
References:
1. BCG, https://www.bcg.com/publications/2024/software-projects-dont-have-to-be-late-costly-and-irrelevant
About the author
Balaji Raghunathan heads Data & AI Engineering for New Accounts at Sigmoid. He has more than 25 years of Global experience in the IT Industry and has played varied leadership roles cutting across Business Technology consulting, IP Commercialization, Enterprise Architecture, Pre-Sales, and Delivery. With his extensive knowledge and experience in Digital Transformation, Data & AI Engineering projects, he helps enterprises in Retail, CPG, Manufacturing, and BFSI extract meaningful insights from data to drive informed decision-making.
