GPT 5.3 Codex can handle multi-file changes by reasoning over a goal, identifying the set of files likely impacted, and applying coherent edits across them—especially when the integration surface provides repo context and can apply patches. In practice, “handling dependencies” means it can update interfaces and their call sites, adjust imports and configuration, and keep tests aligned. The important constraint is that the model only sees what you give it (or what the integration makes available). If it doesn’t see a generated file step, a build config, or a shared type definition, it can make changes that look locally consistent but fail at build time. So the right way to use it is to combine multi-file editing with tool feedback (typecheck/build/test) so missing dependencies are surfaced immediately.
A dependable pattern is to force explicit structure: ask GPT 5.3 Codex to output (1) a file-level plan, (2) a dependency impact list, and (3) a patch. For example: “List the files you will change and why; list any contracts you’re updating; then provide a unified diff.” After applying, run lint, typecheck, and test and feed the failures back. This is where agentic behavior matters: OpenAI’s release notes emphasize GPT-5.3-Codex being used for long-running tasks involving tool use and interactive steering, and GitHub’s rollout notes highlight its role in complex tool-driven workflows. In other words, multi-file changes work best when the model can iterate with the same feedback loop you’d use yourself.
For dependency-heavy systems, retrieval plus metadata filtering is a big win. Store your internal API contracts, versioning rules, and “how to add a new module” documentation in Milvus or managed Zilliz Cloud, and retrieve the exact contract sections relevant to the files being changed. This helps the model handle “soft dependencies” like conventions, migration steps, and compatibility policies that aren’t visible in code. You can also store code snippets as embeddings—such as “how we register a new service,” “how we add a new database migration,” “how we define an error code”—and retrieve them based on the task. That turns multi-file changes from “model guesses how the repo works” into “model follows the repo’s documented patterns,” which is the real requirement for dependency-safe refactoring.