Comment by manbash

Under "Programmatic Tool Calling"

> The challenge

> Traditional tool calling creates two fundamental problems as workflows become more complex:

> Context pollution from intermediate results: When Claude analyzes a 10MB log file for error patterns, the entire file enters its context window, even though Claude only needs a summary of error frequencies. When fetching customer data across multiple tables, every record accumulates in context regardless of relevance. These intermediate results consume massive token budgets and can push important information out of the context window entirely.

> Inference overhead and manual synthesis: Each tool call requires a full model inference pass. After receiving results, Claude must "eyeball" the data to extract relevant information, reason about how pieces fit together, and decide what to do next—all through natural language processing. A five tool workflow means five inference passes plus Claude parsing each result, comparing values, and synthesizing conclusions. This is both slow and error-prone.

Basically, instead of Claude trying to, e.g., process data by using inference from its own context, it would offload to some program it specifically writes. Up until today we've seen Claude running user-written programs. This new paradigm allows it the freedom to create a program it finds suitable in order to perform the task, and then run it (within confines of a sandbox) and retrieve the result it needs.