AI coding agents face limits on the amount of code they can process at once, which can quickly consume token or usage limits when large files are fed directly into a language model. To work around these constraints, developers fine‑tune models to generate auxiliary scripts that extract needed data, allowing the agents to operate on smaller, targeted inputs. Techniques such as dynamic context management and context compression let agents summarize past interactions, preserving essential details while discarding redundant information. These approaches enable semi‑autonomous tools like Claude Code and OpenAI Codex to handle complex codebases more efficiently without overwhelming the underlying model. Leer más →