Dynamic context discovery

Introduced dynamic context discovery as a pattern for AI agents, contrasting it with static context. This pattern involves treating various data sources (long tool responses, chat history, agent skills, MCP tools, terminal sessions) as files that agents can dynamically access. This improves token efficiency and response quality. Specific implementations include writing long tool responses to files, using chat history files for summarization, dynamically loading MCP tools based on task needs (reducing agent tokens by 46.9% in A/B tests), and syncing integrated terminal outputs to the filesystem.