Micro-Turns for Efficient Context and State Management in Multi-Hop Retrieval Agents

Long-running retrieval agents must continually decide what context and state to preserve, compress, rank, or discard. In many harnesses, these decisions are handled by fixed programmatic rules that truncate old messages, apply hand-written compression, or evict content when a capacity limit is reached. We introduce _micro-turns_, short harness-injected model calls dedicated to context and state management between main agent turns. A micro-turn branches from the current trajectory, has the model perform a scoped management operation, and returns a compact state update. After applying the update, the harness discards the micro-turn prompt and response rather than recording them in the persistent trajectory. This makes context and state management model-mediated without lengthening the main session or exposing later main turns to auxiliary management exchanges. We instantiate micro-turns in a multi-hop retrieval agent built on the Harness-1 retrieval framework, where they summarize retrieved chunks, resolve curated-set overflow, and order curated evidence. We empirically show that across a diverse suite of retrieval benchmarks with Qwen3.5-4B, micro-turns improve recall while reducing the persistent-context token and attention cost of every episode, and that ordered curation further strengthens ranking at a bounded, dataset-dependent comparison cost, suggesting a practical harness primitive for efficient, context-heavy retrieval agents.