Agent Context Pruning: How Rovo Dev keeps long sessions useful

This post introduces the concept of agent context pruning as a core capability for maintaining long-running AI agent sessions. It details a structured, multi-stage pruning process that prioritizes least-destructive operations, such as trimming large tool outputs and redundant intermediate steps, before resorting to more aggressive methods like summary collapse. The system protects the beginning of the conversation (task definition) and the most recent interactions (local coherence). It contrasts this structure-aware pruning with LLM-based compaction, highlighting the former's advantages in speed, cost, and preservation of original conversation structure. The post emphasizes that pruning is essential for agents to handle complex software tasks over many interactions by intelligently deciding what to remember, compress, or forget.