Abstract
Digital publishers may face challenges in interpreting complex performance data to identify technical issues or monetization opportunities. The disclosed technology describes a system that can utilize a multi-agent artificial intelligence architecture to ingest and process varied data sources to detect anomalies. An orchestrator agent may delegate analysis to specialized sub-agents, which can use a retrieval-augmented generation engine grounded in a corpus of verifiable data to perform root cause analysis and formulate solutions. The system can present findings in a narrative format and may include an action engine to facilitate remediation, assisting users in converting performance data into actionable strategy and providing scalable support.
Creative Commons License
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Recommended Citation
N/A, "Grounded Multi-Agent AI System for Automated Publisher Analysis and Remediation", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11483