Organizing Collaboration Around Contributions: Agentic Intellectual Property for Long-Horizon Multi-Agent Systems

Long-horizon tool use requires more than generating and combining candidate responses: agents must identify which contributions add grounded information to subsequent decisions. We introduce Agentic Intellectual Property (AIP), a framework that organizes multi-agent collaboration around attributable contributions rather than primarily around agent identities, specialized roles, or communication topology. At each environment state, agents contribute Action, Evidence, or Synthesis, enabling collaboration through executable proposals, supporting or opposing reasons, and new decision-relevant conclusions. Independent peer reviews assess contribution novelty, usefulness, and validity, assign credit, and govern admission to a shared ledger. An independent Final Judge uses the current state and retained contributions to generate the sole action committed to the environment. This design encourages useful exploration while seeking to limit the propagation of redundant or unsupported content, using the same backbone across all roles and no counterfactual environment branches. Evaluations on BFCL v4 multi-turn and ToolHop show that AIP achieves the highest overall accuracy among all evaluated single-agent and multi-agent baselines with both Qwen3-4B-Instruct and Llama-3.1-8B-Instruct. AIP reaches 17.5% and 40.1% accuracy with Qwen, and 14.3% and 11.0% with Llama, on the two benchmarks, respectively, while recording fewer average tool errors per task than multiple competing architectures. These results support contribution representation, evaluation, and selective propagation as a complementary design axis for improving long-horizon multi-agent collaboration.