Your AI Agent Changed One Repo. Did It Check What Breaks Outside It?
Hello Devs 👋 One thing I have noticed with AI coding agents is that they are very good at working inside the repository you give them. You ask the agent to change an API, update a database model, add a field, modify a service, or change some business logic. It searches the code, finds the relevant files, makes the changes, runs some tests, and gives you a result. For a small application, that…
AI coding agents excel at making changes within a repository, such as updating an API, adding a field, or modifying business logic. However, these agents often struggle when changes affect other systems, like shared APIs, front-end applications, SDKs, or background workers. For instance, if an AI agent updates a payment response to include transaction status, it might not be aware that other repositories depend on the exact shape of that response.
Similarly, if the agent modifies a timeout value in the payment service, it may not consider the impact on checkout-web, order-api, or payment-provider.
Developers typically have a deeper understanding of the system's context after working on it for years, but AI agents lack this memory. While AI agents can search for references, imports, and tests, they cannot automatically grasp the dependencies and external relationships. The Context Engine, developed by Qodo, addresses this issue by utilizing repository relationships, pull request history, specifications, and live Git state.
This engine helps AI agents understand how changes fit into the broader system. For example, if a developer asks the agent to change the payment timeout, the context-aware workflow would first investigate which repositories consume the value, where the API contract is defined, and if there are related pull requests or tests in other repositories.
This approach mimics how an experienced engineer would approach the change, without requiring manual discovery of every connection.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
