Sourcegraph’s AI story is no longer one product. Cody, enterprise code intelligence, and the independent Amp agent business reveal different distribution and margin structures.
Augment Code made large-repository context its central engineering problem, building real-time personalized indexes and retrieval systems so coding agents could work across long-lived enterprise codebases.
Sourcegraph's Cody built AI coding around repository and code-graph context, using Sourcegraph search to retrieve relevant code from large and multi-repository codebases before asking an LLM to answer or generate.
CodeBERT treated programming languages and natural language as a joint pretraining problem, helping establish source code as a first-class domain for Transformer models before generative coding assistants became mainstream.