What Does an Agent Need? Bundle-Aware Agent Resource Composition

As catalogs of models, tools, and reusable skills expand, determining which resources an agent should use becomes increasingly challenging. Tool-selection knowledge embedded in model parameters can lag behind catalog changes, while semantic top-$k$ retrieval reduces context length but may retain noise and exclude complementary resources needed to satisfy implicit task requirements. Beyond retrieving relevant resources or reusing predefined agents, how to compose heterogeneous resources for a new task remains underexplored. We formulate Task-Conditioned Agent Resource Composition (AgentComposition): jointly selecting a backbone model and composing a tool–skill bundle to meet task requirements. We propose Retrieval-Grounded Bundle-Aware Agent Generation, which retrieves resources and historical configurations as compositional evidence, recombines resources through constrained beam search with a fine-tuned generator, and reranks complete configurations using generator likelihood and a bundle-aware critic. Evaluated against reference configurations, our method achieves rank-discounted component recall and precision at 10 (RDCR@10/RDCP@10) of $60.00\%/49.18\%$ on AgentSelect and $68.11\%/58.30\%$ on SkillsBench. The results also reveal that leading LLM baselines can select relevant resources but struggle to recover complete configurations. Controlled transfer experiments show that AgentSelect-trained initialization improves adaptation to SkillsBench using only four target-domain queries, while end-to-end evaluation shows that predicted configuration rankings broadly align with execution quality. These results support resource composition as a learnable and transferable capability for task-specific agent construction. Our work shifts the focus from individual resource relevance to joint task suitability. AgentComposition thus offers a path toward more precise and personalized agents that adapt to changing resource ecosystems and continually evolve through accumulated task experience.