Effective context pruning for agents requires preserving prompt cache structure—TokenPilot achieves 56-87% cost reduction by removing content conservatively rather than aggressively rewriting prompts.
TokenPilot manages context in long-running AI agents by smartly removing unnecessary information while keeping the prompt cache valid. It uses two strategies: cleaning up noise when information enters the system, and removing old context only when it's no longer useful. This cuts inference costs by 56-87% while maintaining performance.