Evolving Multi-Agent Systems through Sense of Agency

Multi-agent systems (MAS) tackle complex tasks by coordinating agents with complementary roles and capabilities. Evolving their individual behaviors and interaction structures during inference enables adaptation to task-specific demands, but requires informative feedback. Existing approaches often rely on external feedback from task-specific verification environments or separate LLM evaluators, limiting evolution when such feedback is unavailable. We propose SOAP, the Sense of Agency Paradigm, which draws inspiration from people's Sense of Agency (SoA)—the feeling that their own actions shape what happens. SOAP turns this intuition into internal feedback: agents anticipate what their actions will achieve and assess whether execution meets those expectations. We formulate computational SoA to capture both an agent's expectation of performing well and how closely its post-execution self-assessment matches that expectation. At individual (i.e., intra-agent) and interaction (i.e., inter-agent) levels, this feedback guides iterative prompt and topology updates toward higher anticipated achievement and smaller prediction–outcome gaps, without external feedback. Experiments on BrowseComp-Plus, Plancraft, WorkBench, and Cloudcast demonstrate superior performance against evaluated baselines, with gains across backbone models. Further analysis highlights the complementary benefits of individual- and interaction-level feedback, supporting SoA as an effective internal signal for improving task completion during MAS evolution. Supplementary materials are available [here](https://anonymous.4open.science/r/SOAP-ICLR2027-supplementary-submission), and a demo video can be downloaded [here](https://anonymous.4open.science/api/repo/SOAP-ICLR2027-supplementary-submission/file/SOAP_demo_video.mp4?v=f637803c&download=true).