Abstract
An autonomous agent configures a configurable enterprise platform to onboard a new business vertical. The agent performs live environment introspection using parallel administrative API calls to build a snapshot and a graph of configuration components and their relationships. Existing verticals are detected using naming patterns and relationship traversal with confidence scoring, and a baseline vertical template is extracted. A three-phase requirement analysis maps requirement patterns to component clusters, matches required components against the snapshot using five-tier classifications (exact, sibling, partial, misconfigured, missing) with risk metadata, and enriches results using a machine-learning model that identifies dependencies and contradictions and outputs structured directives. A dependency DAG is generated to produce a tiered phased deployment plan with rollback annotations. Metadata artifacts are generated from parameterized templates and deployed according to the plan, and outcomes are used to update patterns and templates.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "AI-Powered Autonomous Agent for Enterprise Platform Configuration with Live Introspection and Dependency-Aware Deployment", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10760