Abstract
An automated insights generator coordinates multiple specialized AI agents to conduct autonomous, iterative exploratory analysis from a single prompt. A central orchestrator executes outside a large language model (LLM) and manages persistent state, tool invocation, and repeated looping by rotating LLM sessions to avoid context exhaustion. A topic discovery agent monitors leadership communications to populate and prioritize a backlog of unaddressed analysis topics. An exploratory analysis agent executes a codified multi-phase methodology using organizational data tools such as SQL engines, experimentation platforms, metric systems, and visualization services. Outputs are cross-checked by a data validation agent against approved dashboards and canonical metric definitions. Persona-based reviewer agents, instantiated from domain-expert communication patterns, provide adversarial, content-specific critiques that must be materially addressed before publication. Semantic novelty checks and Bayesian confidence layering gate publication, including higher evidence thresholds for insights that contradict existing knowledge, resulting in publication-ready analytical documents.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Automated Insights Generator", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10644