Master thesis · Siemens AG, Amberg
Nuremberg, Germany
Personal studio · agentic-systems-studio.com
AI Engineer building agentic systems for industrial and product use. Local-first agent environments, multi-agent ops, evaluated retrieval — built to sit on a plant floor or a product surface, not a slide.
Industrial
Siemens Amberg thesis on multi-agent root-cause analysis. FAPS research on local RAG with trustworthiness metrics.
Agent environments
Terminal grids, kanban dispatch, shared memory. Agents that run as processes — Claude, Cursor, Codex — not chat toys.
Evaluated retrieval
Citations, refusal, recall@k, MRR. Answers that can point at a source, or admit they cannot.
Work
Each dossier is a dedicated route. Copy is from the public repos — no star counts, no invented metrics.
7 projects
Mission control for AI coding agents. Real PTYs in a grid, a board that launches them, and memory they can share — TypeScript and Tauri, not a chat wrapper.
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A local command center for agents already on the machine. Honest status. Dry-run until you turn execution on.
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Public product page for Agent Fleet — orchestration, RAG, MCP, observability, and portable deploy paths. The fleet codebase is not a public repository.
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A second agentic development environment, kept as its own repo on purpose. Real PTYs, grid presets, kanban dispatch, shared memory MCP.
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The retrieval upgrades that sit between naive RAG and production, instrumented so you can watch each stage — and measure whether they help.
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A deployed multi-agent product: cited RAG, a hand-rolled research tool loop, evidence-anchored tailoring, metering, and an LLM-as-judge suite.
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The origin system. Chat with PDFs, cite the chunks, and score answers — built to understand every moving part before scaling the ideas up.
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Paper
Local knowledge retrieval for industrial settings, with trustworthiness and verification metrics in the evaluation loop — not retrieval quality alone.
DVS EBL 2026 · doi:10.53192/EBL20260344