Abstract
Techniques are disclosed for organizational meta-learning and adaptive escalation in autonomous multi-agent project execution. A project signature is generated from features such as scope, domain, team size, duration, parallelism, risk, and novelty. An organizational pattern, including team structure, channel structure, governance structure, and workflow sequence, is retrieved from a pattern library using signature similarity and an effectiveness score computed from prior outcomes. The project is configured and executed using the retrieved pattern. An adaptive escalation controller determines whether to escalate decision points to a human based on decision category, complexity, risk, project phase, agent confidence, and per-human calibration, and records outcomes including substantive correction and rubber-stamp approval. After execution, a pattern extraction engine captures the used organization and outcomes, updates pattern scores, and stores anti-patterns with diagnostics when repeated low performance is observed.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Organizational Meta-Learning and Adaptive Escalation for Autonomous Multi-Agent Systems", Technical Disclosure Commons, (June 30, 2026)
https://www.tdcommons.org/dpubs_series/10656