Agents stumble as codebases grow due the size, the layers, the accumulated history. Devramp takes a different approach: it learns the structure of your code ahead of time. Every symbol, file, and directory is summarized and kept up to date, so the agent never starts from zero. When it tackles a task, it navigates top-down: from modules, to files, to functions, assembling a coherent picture instead of fumbling through fragments. The result is an agent that stays oriented, works efficiently, and remains reliable even in sprawling, long-lived systems.

Simple edits are one thing, most agents can manage those with the right prompt. The difficulty comes when a task spans multiple files, crosses abstractions, or requires an understanding of how different parts of the system interact. With limited context, agents lose track, make incorrect assumptions, or re-implement functionality that already exists. Devramp addresses this by giving agents a structured understanding of the codebase. Every symbol, file, and directory is analyzed in advance, with summaries kept up to date as the code evolves. When the agent begins work, it can navigate this condensed model of the code top-down, from directories, to files, to functions, assembling a consistent picture before making changes. That structure lets agents work more reliably on tasks that go beyond quick fixes. Refactors, cross-cutting updates, or new features that touch several modules become manageable. Instead of avoiding these harder problems, you can delegate them with more confidence.