When teams build AI agents that work with code, the parsing layer rarely appears in the architecture diagram, even though it should. Every language your agent touches needs a parser. Every parser has its own grammar format, its own compilation toolchain, and its own quirks around error recovery. When you're building a coding agent that handles Python, TypeScript, and Go, you're already maintaining three separate parsing dependencies before you've written a single line of agent logic. Now add Rust, Java, and Ruby to the mix, and you have a dependency management problem dressed up as an infrastructure problem. This is the invisible tax on code-aware AI systems. This blog is all about making it visible and showing how our tree-sitter-language-pack eliminates it at the infrastructure layer. The parser tax no one budgets for Most code-aware agents today handle parsing one of two ways: they treat source files as plain text, or they bolt on language-specific parsing libraries one at a time.…