How Siemens "slices the elephant," advancing agentic workflows for industrial software development

With codebases spanning hundreds of millions of lines developed for over more than a decade, Siemens faced a challenge that standard AI tools couldn't solve: understanding and modernizing this code and the applications which run on it. The scale and depth of industrial-grade software demand a fundamentally different approach. Existing coding assistants lacked the contextual depth required to navigate complex, multi-layered industrial codebases - a gap Siemens set out to close.

To solve this, Siemens and Google Cloud created Knowledge Fabric , an AI system for automating the software development lifecycle. It was built using knowledge graphs on Spanner Graph, the Google Agent Development Kit, Gemini API, Agent Platform, Gemini CLI, and Anthropic Claude Code. In a pilot migrating existing frontiers to web-based interfaces, Knowledge Fabric reduced implementation effort, freeing engineers to focus on customer innovations while maintaining full system compatibility.

"By ingesting the entire software ecosystem into an intelligent agentic system equipped with custom knowledge graphs, we aren't just helping developers optimize their development time; we are enabling autonomous agents to reason across the past to build the future," said Franz Menzl, senior vice president, product creation excellence at Siemens. "This is about freeing engineers from repetitive work so they can focus on higher-value problem solving. " The challenge: the complexity industrial software Modernizing large-scale industrial-grade software systems is often compared to rebuilding a jet while flying it.

For Siemens, the challenge had four dimensions: Scale: The repositories are massive - far exceeding the context windows of standard large language models. Fragmentation: Critical knowledge was scattered across code, Jira tickets, Confluence pages, and scanned PDF manuals from the early 2000s. Complexity: Tracing the link between a specific line of code and a functional requirement document from 10 years ago presented a challenge that no manual or conventional tooling approach could address efficiently.

Hallucinated or unvalidated changes are not merely inefficient but operationally unacceptable. "We realized that standard RAG (retrieval-augmented generation) wasn't enough," said Agata Gołębiowska, technical lead, Google Cloud. "Code isn't just text; it has inherent structure.

A class belongs to a file, which belongs to a module. Flattening that into a vector database meant losing the representation of relationships elements of the codebase. " The solution: A domain-aware Knowledge Fabric To make this sprawling software environment navigable for AI-driven workflows, the teams built the Knowledge Fabric agent.

This agent goes beyond keyword matching to "understand" the relationships between assets. We use Spanner Graph to model the inherent structure of the codebase, applying the same rigor to documentation across formats. By mapping connections between these domains, we can link specific code snippets directly to requirements in a design document.