Abstract
This invention introduces a predictive execution intelligence architecture that governs autonomous AI actions prior to production execution within enterprise environments. Unlike existing approaches based on permissions, prompt filtering, sandboxing, or post-execution monitoring, the proposed system evaluates potential enterprise impact before execution occurs.
Autonomous AI agents increasingly perform real-world operations such as workflow orchestration, infrastructure updates and transactional processing. Existing governance systems lack the ability to predict downstream consequences, leading to unintended state changes, cascading workflow failures, compliance risks and operational instability.
The proposed system introduces a synchronized execution twin representing enterprise state and dependencies. Autonomous actions are simulated within this twin to compute state-differential impact, cascade propagation and reversibility characteristics. Based on this analysis, the system deterministically allows, restricts, transforms, escalates, or blocks actions before execution.
Key innovations include predictive state evaluation, cascade-aware modelling, adaptive action mutation, reversibility intelligence, deterministic commit validation and verifiable execution provenance. This architecture establishes a new category of enterprise AI governance, improving reliability, auditability and scalability of autonomous systems.
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Recommended Citation
INC, HP, "Autonomous Predictive Execution Intelligence Architecture for Agentic Systems", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10889