I built an agentic assistant for battery engineering. A physics-based digital twin runs the actual electrochemical simulation, an LLM orchestrates and explains. Partway through, I ran into a failure that had nothing to do with the physics or the prompt wording. It was about how many tool calls I was asking a 7B model to plan in one go, and it changed how I designed every tool after that.

What the system does

The principle behind the whole project: physics does the maths, the model does the language.

A PyBaMM digital twin simulates an LG M50 21700 cell using the open Chen2020 parameter set — a real electrochemical model, not a language model's best guess at a discharge curve. That twin is wrapped as six tools and exposed through FastMCP over stdio, so it's not hard-wired into one agent; any MCP client can drive it. On the other side, a LangGraph ReAct agent, backed by a local Ollama model, reads a plain-English question, decides which tool to call, and turns the physics result into an explanation.

The six tools: