Context Management
Context engineering for long-horizon, multi-step agents.
Techniques for keeping an agent’s working context small, relevant, and cheap across long tool-use trajectories.
- Layered loading so only task-relevant references and history enter the prompt.
- Summarization and selective recall to bound context growth over many steps.
- Tight coupling with the memory layer, so retrieval and context selection reinforce each other.
Developed during my AI research internship at TikTok (ByteDance).