Dynamic context discovery

Coding agents are rapidly transforming software development by leveraging improved agentic models and advanced context engineering techniques to enhance their capabilities. Cursor has optimized its agent harness for each frontier model it supports, introducing dynamic context discovery to improve token efficiency and response quality by allowing agents to pull only relevant context rather than relying on static context. This approach is applied through various strategies, such as turning long tool responses into files, referencing chat history for better summarization, supporting the Agent Skills open standard, efficiently loading only needed MCP tools, and treating integrated terminal sessions as files. The use of files as a simple abstraction helps manage context bloat and enhances the agent's ability to find relevant information efficiently, leading to significant reductions in token usage and improved communication about tool status. These innovations, developed by a team at Cursor, demonstrate the potential for more ambitious AI-driven coding tasks, with further advancements expected soon.