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

Siemens and Google Cloud created Knowledge Fabric, an AI system using knowledge graphs and agentic workflows to modernize industrial software development. The system reduced implementation effort in a pilot by enabling autonomous agents to reason across legacy codebases, freeing engineers for higher-value work.

For technology companies like Siemens, software is the nervous system of factories, energy grids, and transportation networks worldwide. As a global leader in industrial AI, industrial software, and industrial automation, Siemens brings decades of domain expertise across factory and process automation, energy infrastructure, and intelligent transportation — expertise that no off-the-shelf AI solution can replicate. But innovation carries a heavy anchor: legacy code. 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.” Modernizing large-scale industrial-grade software systems is often compared to rebuilding a jet while flying it. For Siemens, the challenge had four dimensions: "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." 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. Agents then traverse this graph, using tools to query the structure via Graph Query Language GQL https://docs.cloud.google.com/spanner/docs/reference/standard-sql/graph-intro . But GQL is only one piece. To enable semantic understanding, we generate embeddings for every node, using Spanner's Approximate Nearest Neighbors ANN https://docs.cloud.google.com/spanner/docs/find-approximate-nearest-neighbors algorithm to perform efficient vector search across the full codebase. Finally, we give agents full-text search https://cloud.google.com/blog/products/databases/spanner-graph-full-text-search?e=0 capabilities, which can be combined with GQL to pinpoint nodes and edges with precision. Combining these three methods lets an LLM agent answer complex queries, such as: "Which functions need to be updated if I change the logic in the Axis Control Panel?" The system traverses the graph — weighing keyword and semantic similarity — to identify dependencies, retrieve relevant documentation, and present a precise impact analysis. This precise context is what lets a coding agent produce a valid, usable, and maintainable implementation. A key insight from the project was that AI agents struggle with massive, ambiguous tasks. To succeed, the team adopted a design pattern dubbed "slicing the elephant." The system breaks a sweeping request like “refactor this module” into smaller, more manageable tasks, each handled by a specialized agent built with the Google Agent Development Kit ADK : The system keeps a human in the loop at every step, which ensures reliable, production‑grade outcomes and keeps engineers focused on meaningful work rather than routine implementation. "By slicing the elephant — breaking complex refactoring jobs into smaller, agent-led tasks — we observed a significant productivity increase," said Alexander Lomakin, project lead at Siemens. "We essentially gave the AI the roadmap it needed to navigate the complexity." Developers saw results almost immediately. Analyzing dependencies for a new feature once required senior engineers to spend several days navigating codebases and legacy documentation. With the Knowledge Fabric, the same work now takes far less time. In a recent production pilot migrating legacy control panels to modern web‑based interfaces, the Knowledge Fabric reduced overall coding effort while preserving system integrity and industrial quality standards. Engineers now spend more time creating customer value and less on repetitive work. The Knowledge Fabric shows that generative AI can do more than write boilerplate code, it can also help teams modernize the legacy systems their businesses depend on most. To learn more about building graph-based agents for your own legacy modernization: