Abstract
A system and method for cross-agent behavioral pattern learning and knowledge reuse from infrastructure interaction exhaust. The system passively captures interaction traces from multiple actors - including autonomous agents, semi-automated processes, and human users - as they interact with databases, APIs, and infrastructure endpoints through heterogeneous surfaces. A semantic inference layer analyzes captured traces to infer properties beyond raw telemetry, including organic field discovery, relevance weighting, prompt-level field alias mining, role frequency and sensitivity determination, and retry-aware intent detection. A pattern sanitization module strips individual actor identity and raw data values while preserving role-scoped organizational tokens, producing parameterized patterns with provenance tags and success or failure context. A reusable pattern library accumulates sanitized patterns over time. A cross-domain pattern detection module identifies second-order patterns spanning surfaces without direct relational connections. A feedback and pattern delivery module closes the loop by delivering refined patterns back to actors in push or pull mode and in real-time or long-term temporal mode, enabling cross-agent knowledge sharing, anomaly intervention, and cross-role semantic bridging.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Zavesky, Eric, "SYSTEM AND METHOD FOR KNOWLEDGE REUSE FROM INFRASTRUCTURE INTERACTION EXHAUST AND CROSS-AGENT BEHAVIORAL METADATA INFERENCE", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/11604