LLM agents turn code interpreters into portfolio sizing engines when you evolve the prompt

Most agent benchmarks evaluate tool use as an information-gathering exercise. You hand a model an API key for stock prices, a search tool for financial headlines, and a Python REPL. Then you watch whether it makes the right API calls before writing a summary paragraph. In practice, giving an agent access to a code interpreter does surprisingly...