On October 29, 2025, Cursor shipped Cursor 2.0 and introduced Composer, its first agentic coding model. Cursor claims Composer is 4x faster than similarly intelligent models, with most turns completing in under 30 seconds. For more clarity and detail, we worked with Lee Robinson at Cursor on this article.
Shipping a reliable coding agent requires a lot of systems engineering. Cursor’s engineering team has shared technical details and challenges from building Composer and shipping their coding agent into production. This article breaks down those engineering challenges and how they solved them.
What is a Coding Agent?
To understand coding agents, we first need to look at how AI coding has evolved.
AI in software development has evolved in three waves. First, we treated general-purpose LLMs like a coding partner. You copied code, pasted it into ChatGPT, asked for a fix, and manually applied the changes. It was helpful, but disconnected.
In the second wave, tools like Copilot and Cursor Tab brought AI directly into the editor. To power these tools, specialized models were developed for fast, inline autocomplete. They helped developers type faster, but they were limited to the specific file being edited.
More recently, the focus has shifted to coding agents that handle tasks end-to-end. They don’t just suggest code; they handle coding requests end-to-end. They can search your repo, edit multiple files, run terminal commands, and iterate on errors until the build and tests pass. We are currently living through this third wave.
A coding agent is not a single model. It is a system built around a model with tool access, an iterative execution loop, and mechanisms to retrieve relevant code. The model, often referred to as an agentic coding model, is a specialized LLM trained to reason over codebases, use tools, and work effectively inside an agentic system.
It is easy to confuse agentic coding models with coding agents. The agentic coding model is like the brain. It has the intelligence to reason, write code, and use tools. The coding agent is the body. It has the “hands” to execute tools, manage context, and ensure it reaches a working solution by iterating until the build and tests pass.
AI labs first train an agentic coding model, then wrap it in an agent system, also known as a harness, to create a coding agent. For example, OpenAI Codex is a coding agent environment powered by the GPT-5.2-Codex model, and Cursor’s coding agent can run on multiple frontier models, including its own agentic coding model, Composer. In the next section, we take a closer look at Cursor’s coding agent and Composer.
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