AI-assisted development
Also known as AI-assisted software development, AI coding
Definition
AI-assisted development is software work that uses an AI system to help with activities such as explaining code, generating or editing code, writing tests, reviewing changes, or navigating a repository.
Assistance appears in many forms
An engineer may ask an assistant to explain an unfamiliar module, generate a test case, or suggest a refactor. An autonomous agent may inspect a repository, edit several files, run checks, and open a pull request. Both fit the broad idea of AI-assisted development, but they create different evidence and review needs.
Adoption is only the beginning
An illustrative organization can report that most engineers have access to an AI coding tool. That says little about how often it is used, which tasks it supports, or whether the resulting work is accepted and maintained.
Evaluate a workflow end to end. Examine useful output, review effort, defects, reverts, delivery timing, and spending. Track foreground assistance separately from background agents when the human involvement and cost structure differ.
Keep attribution honest
Tool logs and commit metadata can support different kinds of attribution. Some tools expose direct usage events. Other contributions must be inferred from timing or authorship signals. Label the evidence and its limitations.
AI can amplify a healthy process and a weak one. If generated changes increase the queue for reviewers, the local speed gain may not improve the system. Read the data with developers and keep customer outcomes in view.
How this relates to Weave
Weave's Token Intelligence connects AI tool usage and spending with engineering output and quality signals. That gives teams a way to study whether assistance changes completed work, review load, and cost. Usage volume alone does not establish a return on investment.
Explore Token intelligence