Two competing approaches have emerged for how AI coding agents build their understanding of a codebase - and the one that's winning isn't the more accurate one.
The first approach is deterministic: a program parses your code the same way a compiler would, tracing every import, every function call, every dependency. Same input, same output, every time. No guessing. The second approach uses the LLM itself (the AI model) to read the code and infer those relationships. Tools built this way often label connections as "INFERRED" and attach confidence scores because the model is, in a sense, making educated guesses.
Deterministic wins on paper. So why are LLM-inferred knowledge graphs pulling ahead in real-world developer tool adoption?
The Parsing Problem
Building a deterministic code map is language-specific, labor-intensive engineering. To accurately trace how a Python module connects to a JavaScript frontend through an API layer, you need custom parsers for each language, framework-specific rules, and constant maintenance as those ecosystems change. A tool that supports 15 languages needs 15 separate parsing implementations, each of which can break when a popular framework updates.
LLM-inferred graphs sidestep this entirely. Point the model at any codebase - Python, Rust, TypeScript, Go, whatever - and it reads the code and builds a map. It might label some connections as "INFERRED" with a 73% confidence score, but it works on day one across every language without custom parsers.
What "Inferred" Actually Gets You
Here's the part that surprises people: LLM-inferred graphs can capture relationships that deterministic parsing misses. A static analyzer tracks explicit imports and function calls. An LLM can notice that function A and function B are always called together in the codebase even though there's no formal dependency between them - and surface that as a likely semantic coupling. That kind of contextual pattern recognition is outside what pure static analysis can do.
The tradeoff is reliability. A deterministic parser is boring in the best way - it either finds the connection or it doesn't. An LLM might hallucinate a relationship that doesn't exist, or miss one that does, particularly in codebases with unusual patterns or heavy use of metaprogramming (code that generates other code at runtime).
Why Inferred Keeps Winning
Speed to market matters. A team building an AI coding tool can ship LLM-inferred graph support in days. Deterministic analysis at broad language coverage is a months-long engineering project. For most developers asking "where does this function get called?" or "what breaks if I change this interface?", 85% accuracy on day one beats 99% accuracy delivered eight months later.
There's also the question of what "good enough" means in practice. AI coding agents use these graphs as context when generating suggestions - they don't need perfect maps, they need maps that are usually right. An agent that confidently recommends the wrong refactoring based on a false dependency is more dangerous than one that hedges and asks the developer to verify.
The case for deterministic isn't wrong. Tools that have invested in it - several static analysis companies have been quietly building this infrastructure for years - hold a real advantage for enterprise codebases where a wrong inference in a 10-million-line system can cascade into production incidents. But for the mass market of developer tools shipping on GitHub and iterating fast, inferred is winning because shipping beats perfect, and most bugs from a wrong inference are caught before they matter.