Abstract
This disclosure describes a self-evolving, multi-agent architecture and associated methods for autonomous digital advertising orchestration, structured as a deeply interconnected network of specialized, goal-directed, and continuously learning agents. Each core function of the digital advertising lifecycle is embodied as an autonomous agent that possesses adaptive, modular capabilities, performs independent decision-making, and communicates via decentralized protocols. The architecture incorporates a synchronized, high-fidelity causal digital twin that models user behavior and competitor actions. This twin is queried by the agents to perform causal counterfactual analysis, massive offline training, and real-time simulation-to-real policy validation. As automated decision-making scales, it is critical to mitigate potential risks such as policy violations or suboptimal user experiences. To ensure platform safety and brand integrity, the framework integrates programmable and verifiable agent alignment using constitutional principles and formal verification. Inter-agent resource mediation is framed using economic principles and internal market mechanisms, enabling highly efficient decentralized capital allocation and fine-grained, privacy-preserving personalization within sub-millisecond serving latency constraints.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Yavary, Aura, "A Self-Evolving, Multi-Agent Ecosystem for Autonomous Advertising", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11336