LAGO: Language Agents with Growing Reusable Options

Language agents based on monolithic tool-use loops struggle with long-horizon tasks that require decomposition, specialized execution, and verification, while conventional multi-agent systems rely on fixed, manually designed roles. We introduce LAGO, a meta-agent framework that represents specialized language agents as reusable temporally extended options. The meta-agent retrieves suitable options from a persistent library or spawns new ones, dispatches them to solve sub-tasks, verifies their outcomes, and admits successful and novel options for future reuse. This turns task decomposition into a reusable capability and allows the effective action space to grow across a task stream without updating model weights. On the full 500-task SWE-bench Verified benchmark, LAGO using frozen Qwen3.5-35B-A3B in both the meta-agent and executor roles achieves a 58.6% resolve rate under single-attempt evaluation with the official Docker evaluator. Cross-model and cross-domain experiments further support the role of decomposition quality, while library analysis shows frequent option reuse and bounded growth under deduplication. These results suggest that reusable agent-options are a promising abstraction for language agents that accumulate reusable task-solving capabilities over time.