Abstract
Disclosed are systems and methods for maintaining operational consistency across autonomous digital, communication, and physical infrastructure. As AI agents, automated network controllers, and robotic systems increasingly take actions on their own, a recurring failure appears: the record of what a system intended, what it observed, and what actually happened in the real world can drift apart. This disclosure describes mechanisms that keep these views aligned, so that every consequential action can be traced from intent to effect, and so that uncertain or incomplete outcomes are recognized and handled rather than silently assumed to have succeeded.
The disclosure presents independently implementable embodiments. In radio access networks, including O-RAN architectures with near-real-time and non-real-time RAN Intelligent Controllers (xApps and rApps), it describes ways to make sure control decisions are tied to observations that are still valid at the moment of action, preventing actions based on stale measurements. For multi-agent AIOps and self-healing infrastructure, it describes coordinated fault diagnosis and remediation in which multiple automated agents do not act on conflicting or outdated conclusions. For interoperator communication, it addresses consistency between session state and charging or accounting records across network and roaming boundaries, relevant to 5G core and IMS environments. For hardware and semiconductor systems, it describes correlating independent hardware observations, such as telemetry and attestation evidence, with the commands that were issued, so that a command is not treated as effective until independent evidence confirms it.
For AI agent ecosystems, the disclosure covers streaming negotiations between agents, as found in agent-to-agent protocols, and the lifecycle of asynchronous tool calls and subscriptions, as found in tool-integration protocols such as the Model Context Protocol, including how to handle tools that respond late, partially, or not at all. For robotics and physical AI, it describes translating abstract robot skills into the actual capabilities and limits of specific hardware before execution. For safety-critical communication, it describes preserving the integrity of instructions across languages, so that translated safety instructions keep their original meaning. For browser-based and computer-use agents, it describes associating each browser action with its resulting effect on server-side resources. Finally, it describes keeping pending operations consistent when an execution context migrates, for example during live migration of virtual machines or containers, or handover between cloud and edge.
Technical implementations may employ typed records, dependency graphs, temporal consistency evaluation, versioned state transitions, protocol-semantic reconciliation, physical-capability constraints, and explicit handling of indeterminate outcomes, meaning cases where it cannot be known whether an action took effect. Industrial applications include telecommunications, 5G and 6G infrastructure, cloud and edge computing, AI agent platforms, semiconductor and hardware-security systems, industrial automation, robotics, emergency communications, and enterprise digital operations. The mechanisms may operate independently or in selected combinations, and do not require particular vendors, platforms, or proprietary interfaces.
Industry relevance: the problems addressed relate to areas in which major technology and telecommunications companies are publicly active, including AI agent platforms and tool-integration ecosystems (for example Google, Microsoft, Amazon, Anthropic, OpenAI, and Meta), accelerated computing and data-center hardware (for example NVIDIA, Intel, and AMD), robotics and physical AI, and open and virtualized radio networks (for example Ericsson, Nokia, Samsung, and Qualcomm). This industry-relevance statement is AI-generated and does not assert that any named company uses, implements, or infringes the disclosed methods; readers must cross-check it against the original PCT filings before drawing any conclusion.
This disclosure is based on the Mothership application PCT/IB2026/055615, filed 4 June 2026 and publicly disclosed 16 July 2026.
The inventor is open to acquihire or strategic collaboration.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Das, Sangam Kumar, "Systems and Methods for Operational Consistency Across Autonomous Digital and Physical Systems", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/12818