This sharing starts from model APIs and tool calling, explaining how Code Agent builds and consumes context through multi-round execution, feedback, and error correction. Combining public protocols, source code, and templates, it sorts out the boundaries between history, state, external data, and current context, analyzes long-context issues, and examines the costs of compression, prefix caching, memory, skills, and sub-agents. At runtime, an auditable append-only history should be maintained, constructing context as a finite derived view: stable instructions and tools form a cacheable prefix, compressed old state is placed in the middle, and the current request along with original evidence stays near the end. Through on-demand retrieval and isolated collaboration, it balances accuracy, cost, latency, and recoverability.
The context management of code agent
KeynoteAbstract
This sharing will systematically guide everyone into the world of large model AI calling. It is suitable for friends who are interested in AI application development and want to understand the internal mechanisms of intelligent agents.