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

Description and use

<p>Most agent benchmarks evaluate tool use as an information-gathering exercise. </p> <p>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 little if the system prompt treats code execution as descriptive color. The model calculates a 20-day annualized volatility, writes down the number, and then allocates capital using whatever vague qualitative heuristics it had before opening t

Problem

<p>Most agent benchmarks evaluate tool use as an information-gathering exercise. </p> <p>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 little if the system prompt treats code execution as descriptive color. The model calculates a 20-day annualized volatility, writes down the number, and then allocates capital using whatever vague qualitative heuristics it had before opening t

Outcome

<p>Most agent benchmarks evaluate tool use as an information-gathering exercise. </p> <p>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 little if the system prompt treats code execution as descriptive color. The model calculates a 20-day annualized volatility, writes down the number, and then allocates capital using whatever vague qualitative heuristics it had before opening t

Evidence

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

Source