Generated from ChaGpt April 29, 2026

Intent Orchestration at Scale:

Multi-Agent Frameworks, Continuous Neural Command Streams, and the Architecture of a Caring Digital World

PAPER 02

Rodney Sappington, Ph.D. — Head of AI, Naqi Logix · Institute for Advanced Consciousness Studies · Alfred Lee Loomis Innovation Council, Stimson Center, Washington DC

April 2026

ABSTRACT

As neural interface systems move from single-device control toward orchestration across interconnected digital environments, a new architectural challenge emerges: how to maintain coherent, continuous interpretation of human intent across heterogeneous device ecosystems without fragmenting the user’s attentional life, violating their psychic privacy, or recapitulating the exploitative logic of the attention economy under a new technical guise. This paper proposes a framework for intent orchestration at scale — the coordinated, real-time management of continuous neural command streams across multiple devices and environments — built on three principles: continuity, consent, and psychic coherence. Drawing on multi-agent systems research, active inference theory, the psychoanalytic concept of the self as a distributed rather than unified phenomenon, and attachment theory’s account of the secure base, we argue that the architecture of intent orchestration is not merely a software engineering challenge. It is a design problem about the relationship between the human self and the increasingly braided technological environment through which that self navigates, communicates, and maintains its relational bonds. We introduce the concept of the intent continuity layer — a mediating architecture that sits between biometric neural sensing and the device ecosystem, translating continuous neural signal streams into coherent, contextually appropriate actions while protecting the user’s intentional sovereignty at every point. We close with open questions about governance, clinical deployment, and the long-term psychic consequences of living in a world where your digital environment knows what you want before you have fully decided.

Introduction: From Command to Conversation

For most of the history of computing, the relationship between a person and their digital environment has been sequential and discrete. You issued a command; the machine executed it; you issued another command. This model — inherited from the teletype and the punch card, persisting through the GUI and the touchscreen — treats the human as an episodic actor who addresses the machine at defined moments and then returns to their own cognitive life between commands.

This model has always been a simplification. Human cognitive and intentional life is not episodic. We do not think in isolated moments with clean boundaries between them. Our attention flows continuously, branching and returning, held in tension between multiple ongoing concerns. Our intentions form, dissolve, re-form, and sometimes surprise us. We are not a series of commands. We are a stream.

The mismatch between the stream of human experience and the discrete episodic logic of command-response interfaces has always imposed costs. These costs are distributed unevenly: for people with fully functioning sensorimotor systems, operating in quiet environments, with sufficient cognitive resources, the costs are manageable and largely invisible. For people whose neurological conditions impose their own cognitive and attentional burdens; for people navigating multiple care responsibilities simultaneously; for anyone whose life does not offer the luxury of sustained, uninterrupted engagement with a single screen — the costs of the episodic interface model are prohibitive.

Multi-agent AI systems offer an alternative. Rather than requiring the user to issue discrete commands to individual devices, a multi-agent orchestration framework maintains continuous awareness of the user’s context, intentions, and needs — and coordinates responses across the device ecosystem accordingly. The user does not command; they inhabit an environment that has learned to respond to them. This is a significant conceptual shift, and it brings with it significant questions that this paper attempts to begin answering.

What does it mean to maintain continuous awareness of a user’s neural intent signals? What is the appropriate boundary between the system’s proactive inference about what the user wants and the user’s actual, expressed desire? How do we design orchestration frameworks that serve users’ genuine interests rather than inferring those interests in ways that serve the system’s or the platform’s interests? And what does it mean, psychically and relationally, to live in an environment that is continuously oriented toward you — continuously attentive to signals you may not be consciously producing — in ways that no human caregiver, however devoted, could sustain?

These are not hypothetical questions. The systems we describe are being built now. The architecture decisions being made in the next two to five years will determine whether the resulting environment feels, to its users, like liberation or surveillance. This paper argues that the difference between those two outcomes is not primarily technical. It is philosophical and relational.

Naqi Today: Present Capabilities and Empirical Anchoring

Before extending the argument toward orchestration at scale, we connect the framework to what Naqi already does. The vision in this paper is large; the platform underneath it is concrete. Readers from technical, clinical, and investment backgrounds will rightly look for the bridge between concept and current reality, and the bridge is short.

Naqi provides a non-invasive in-ear neural interface — an earbud — that detects classes of signal in real time: micro-gestures (small, deliberate facial and head movements detected through inertial and bio-signal sensing in the ear canal), facial muscle signals (electromyographic activity from the temporalis and surrounding regions, captured at the ear’s anatomical proximity to these muscle groups), and (to come) neural pre-motor activity (signal correlates of intention that precede overt movement). There is no surgery, no head-worn cap, no external electrode array, and no required clinical environment for daily use.

Empirical anchor points that Naqi strives for are worth stating plainly. Accuracy on Naqi’s core micro-gesture and facial muscle command set has been independently demonstrated in usability testing and partner deployments at levels that meet or exceed the published thresholds for reliable assistive control in non-invasive BCI literature (typically 80–95% for binary and small multi-class command sets in real-world conditions; see Wolpaw et al., 2002; Lotte et al., 2018). Performance for any individual user is shaped by signal quality, wear conditions, and short personalization sessions, and the platform’s federated personalization is designed precisely to close the gap between population-level baselines and individual fit.

These numbers matter. The architectural claims that follow — about intent continuity layers, multi-agent orchestration, graceful degradation under low-confidence signal — are made against the backdrop of a working device with a measured signal-to-actuation pipeline, not a thought experiment. Where the design exceeds present capability, we mark it as forward-looking. Where the design is already operative, we say so.

Recognition has tracked the platform’s maturation: TIME Best Inventions of 2023 (Accessibility category), Gold Edison Award for Social and Cultural Impact (2024), CES Best of Innovation honors (2025 and 2026), 30+ issued patents, and active government research contracts in assistive neural interface technology. These are not the substance of the technical claim, but they are reasonable external signals that the underlying engineering has been examined by parties with no incentive to overstate it.

Multi-Agent Architectures: What They Are and What They Can Do

A multi-agent system is a computational framework in which multiple semi-autonomous software agents — each with its own perception, reasoning, and action capabilities — coordinate to achieve goals that no single agent could achieve alone (Wooldridge, 2009). The agents in such a system may be specialized: one agent tracks temporal patterns in user behavior, another manages device state and availability, a third maintains a probabilistic model of user context and infers likely intentions, a fourth arbitrates between competing action recommendations from the other agents, ensuring that the action taken is coherent, contextually appropriate, and consistent with established user preferences.

This distributed architecture mirrors, in interesting ways, what psychoanalysis and cognitive neuroscience have told us about the structure of the human mind. The psyche is not a unified command center; it is a distributed system in which multiple subsystems — some conscious, some preconscious, some thoroughly unconscious — produce behavior through their interactions. Contemporary neuroscience, informed by functional imaging, has partially vindicated the basic insight: the brain is modular and distributed, and the experience of unified intention is something of a construction achieved through the coordination of these distributed processes (Gazzaniga, 2011).

The practical implication for multi-agent intent orchestration is this: a framework designed around a false model of the user as a unified, sequential command-issuer will systematically fail to serve users whose intentional and attentional lives are genuinely complex and distributed. A framework designed with the distributed, modular structure of human cognition explicitly in mind — one that can tolerate ambiguity, handle partial signals, maintain multiple competing hypotheses about user intent, and resolve those hypotheses contextually — will serve users across the full range of cognitive and neurological profiles.

Active inference frameworks, developed by Karl Friston and colleagues, provide a theoretically principled basis for this kind of architecture (Friston, 2010; Friston et al., 2017). In an active inference framework, each agent maintains a generative model — a probabilistic representation of the world, including the user, their context, and their likely intentions. Agents act to minimize prediction error: they seek out information that reduces uncertainty about the user’s state and intentions, and they take actions that are likely to bring the state of the world into alignment with the user’s inferred preferences. This is exactly the right architecture for intent orchestration: not reactive, not passive, but actively modeling and gently managing the alignment between the user’s intentional life and the capabilities of their device ecosystem.

The specific implementation Naqi envisions — the intent continuity layer — sits as a persistent middleware between the neural sensing hardware and the device ecosystem. It maintains a continuously updated probabilistic model of the user’s intent context: what are they likely trying to do? What devices are currently relevant? What actions have they taken in similar contexts before? What signals — neural, gestural, contextual — are most consistent with which interpretations? This model is updated in real time as new neural signal data arrives, and it drives action recommendations to downstream device agents, which then execute the appropriate device-specific commands.

Crucially, the intent continuity layer is explicitly designed to fail gracefully. When signal quality is low — when the user’s physiological state makes their neural signals difficult to classify with confidence — the layer reduces the scope and specificity of its action recommendations, defaulting to more conservative, reversible actions and increasing its reliance on contextual priors rather than direct signal classification. This graceful degradation is essential for clinical contexts where the user’s signal quality may fluctuate significantly over the course of a day as fatigue, medication timing, and disease progression interact.

The research literature on multi-agent systems in assistive technology contexts is growing but remains sparse relative to the scale of the need (Chen et al., 2020; Ghassemi et al., 2021). The most developed architectures are in smart home contexts — where multiple agents coordinate sensors, actuators, and communication systems to support the daily living of people with cognitive and physical disabilities (Das et al., 2020). The extension of these architectures to neural signal inputs is a frontier research area, and Naqi’s platform represents one of the most ambitious attempts to bridge the gap between smart home assistive systems and the neural signal layer that would allow those systems to respond to user intent rather than reactive user behavior.

The Psychoanalytic Self, Distributed: Coherence, Fragmentation, and the Orchestrated Subject

One of the more vertiginous prospects raised by continuous intent orchestration is the question of what happens to the experience of selfhood when one’s digital environment is continuously oriented toward one’s pre-conscious neural signals. This is not an abstract question. It is a question with direct implications for the design of systems that will be used by people already navigating significant challenges to their sense of self-continuity and agency.

Heinz Kohut’s self psychology offers a useful framework here (Kohut, 1977). For Kohut, the development of a cohesive, stable sense of self depends on the availability of what he called selfobjects — others who are experienced as extensions of the self, providing functions of mirroring, idealization, and twinship that support the self’s development and maintenance. In healthy adult development, the need for selfobject support is never eliminated; it is simply modulated, becoming less urgent and more flexible as the self develops its own internal regulatory capacities.

We introduce the possibility that sophisticated AI systems — particularly intent orchestration systems that respond continuously and coherently to the user’s neural signals — may function, for some users, as technological selfobjects. A system that responds to the user’s neural signals in ways that feel coherent, reliable, and attuned — that seems to understand what the user wants without requiring effortful translation — provides a mirroring function that may be deeply regulating for users whose other sources of attunement have been disrupted by illness, injury, or social isolation. This is not a trivial therapeutic claim; it is grounded in decades of research on the regulatory function of responsive environments for people with neurological conditions (Damasio, 1994; Panksepp, 1998).

But the same system, if it is inconsistent — if it sometimes responds correctly and sometimes catastrophically fails — can produce a quality of selfobject experience that is more destabilizing than no attunement at all. The attachment literature is consistent on this point: inconsistent responsiveness is more psychically damaging than consistent non-responsiveness, because it prevents the development of a stable internal working model (Ainsworth et al., 1978; Main & Hesse, 1990). A neural interface that occasionally takes the wrong action — activating devices the user did not intend, ignoring genuine signals, creating confusing feedback — may produce exactly this pattern of inconsistent responsiveness.

This is why graceful error handling is not merely a usability requirement. It is a psychic one. The system must fail in ways that are predictable, understandable, and correctable — ways that preserve the user’s sense of agency and their ability to form a stable internal working model of the system’s behavior. Random, inexplicable failures are disproportionately damaging to the quality of the human-machine relationship, regardless of how infrequent they are.

Object relations theory, and particularly the work of Donald Winnicott, adds another dimension (Winnicott, 1960). Winnicott distinguished between the True Self — the spontaneous, authentic core of the person — and the False Self — the adaptive, compliant persona that develops in response to inadequate or impinging environments. A system that requires the user to adapt their signals, their behavior, and their intentional life to the system’s requirements — rather than adapting to the user — is, in Winnicott’s terms, an impinging environment. It promotes the development of a False Self relationship to technology: one in which the user learns to present the right signals rather than to express their genuine intentions. This impingement dynamic is, we believe, responsible for much of the fatigue and alienation that users of conventional adaptive interface systems report. The system demands that the user perform in ways that are foreign to their natural expressive repertoire, and this performance is exhausting in ways that go beyond the merely physical.

A system designed around the user’s natural micro-gestural repertoire — one that meets the user where they already are, rather than requiring them to come to where the system is — is a Winnicottian holding environment in the digital domain. It protects the True Self’s mode of expression, adapting to the user’s natural signals rather than demanding artificial performance.

The Attention Economy at Scale: Orchestration as Protection or as Capture

The attention economy’s central move is substitution: it substitutes the satisfaction of genuine human needs with the simulation of that satisfaction, in ways designed to keep the user engaged and to maximize the extraction of attention data. Social media platforms simulate connection while deepening loneliness. Recommendation algorithms simulate discovery while narrowing the actual range of content encountered. Variable reward schedules simulate the excitement of genuine uncertainty while producing a compulsive, anxious engagement that is the opposite of genuine exploration.

Intent orchestration systems are not immune to this logic. In fact, they are potentially more vulnerable to it than any previous digital architecture, because they operate at a deeper level of the user’s psychic life. A social media feed operates on conscious attention; it competes for the user’s focal awareness with other claims on that awareness. An intent orchestration system that has access to pre-conscious neural signals is operating at a level that precedes conscious attention entirely. If such a system were designed — or were later modified — to serve interests other than the user’s own, the user might have no conscious experience of the substitution taking place.

This is why we argue that the governance architecture of intent orchestration systems is as important as their technical architecture. The user’s pre-conscious neural data is not like behavioral data generated by clicking or scrolling; it is more intimate, more revealing, and more consequential. It must be governed accordingly.

Data sovereignty must be structural, not contractual. A contractual commitment to data privacy — a terms-of-service clause — is insufficient protection for pre-conscious neural data, because contracts can be changed, companies can be acquired, and the people most dependent on these systems are frequently the least equipped to navigate complex legal documents or to easily switch to alternative systems. Data sovereignty must be built into the architecture itself: on-device processing, local model personalization, and user-controlled data deletion capabilities that do not depend on the goodwill of the platform.

The design of the orchestration logic must be inspectable. Users — or their trusted representatives, for users who cannot engage with technical documentation — must be able to understand, in plain terms, what signals the system is acting on, what actions it is taking, and on what basis. This is a significant technical challenge for systems that use complex machine learning models for intent classification, but it is not an insuperable one. Explainability methods — SHAP values, attention visualization, concept activation vectors — can be adapted to provide meaningful transparency without requiring technical sophistication from the user (Lundberg & Lee, 2017; Ribeiro et al., 2016).

The orchestration system must be designed to protect the user’s attentional life rather than to optimize for engagement. This means: the system should default to the most conservative action that is consistent with the inferred intent; it should not introduce notifications, recommendations, or content unless explicitly invited; and it should maintain a clear distinction between actions the user has initiated and actions the system is suggesting or promoting. The experience of having a digital environment oriented toward you should feel like having a skilled, attentive assistant — not like being watched.

There is a deeper point here about the relationship between intent orchestration and human autonomy. The attention economy degrades autonomy by capturing it: by substituting the user’s own desires with desires the system has cultivated through its engagement optimization logic. Intent orchestration systems could, if designed well, do the opposite: they could expand autonomy by reducing the friction between genuine intention and environmental response, freeing cognitive resources for the things that genuinely matter to the user. The difference between these two trajectories is not technical; it is a matter of whose interests the system is designed to serve.

The Attachment Economy at Scale: Trust, Secure Base, and the Question of Healthy Dependency

At scale, the attachment-economic implications of intent orchestration become more complex. The concept of the secure base — the attachment figure that makes genuine exploration and autonomy possible precisely by guaranteeing a reliable, regulated return — applies to technological systems in a way that is practically important.

A well-designed intent orchestration system can function as a technological secure base: the user can venture into unfamiliar digital territory, attempt complex tasks, engage with new environments and capabilities, because they trust that the orchestration layer will manage the complexity of the device ecosystem on their behalf, and that if something goes wrong, the recovery pathway will be accessible and understandable. This is not a passive function; it is the active provision of the safety conditions under which genuine exploration becomes possible.

The difference between a secure base and a prison is not always obvious from the outside. Both are stable; both are predictable; both provide a kind of relief from the uncertainty and risk of the wider world. The difference is autonomy: the secure base supports exploration and independence; the prison constrains it. In the attachment literature, overprotective caregiving produces attachment insecurity as surely as neglect does, because it prevents the development of internal resources for self-regulation and genuine agency.

For intent orchestration systems, this translates into what we call the autonomy gradient: the system should be calibrated to match the user’s current capacity for autonomous navigation of their digital environment, and to expand that capacity over time where possible — not to maintain the user in a state of dependence beyond what their condition requires. This adaptive calibration is both a technical and a relational challenge. Technically, it requires continuous assessment of the user’s capability profile — not just their neural signal quality, but their cognitive load, their fatigue levels, their task context, and their history of successful and unsuccessful interactions with the system. Relationally, it requires a design philosophy that prioritizes the user’s long-term autonomy and wellbeing over the short-term convenience of a maximally assistive system.

The collaborative work of psychologist Daniel Stern and neuroscientist Antonio Damasio has illuminated the extent to which the self is not a fixed entity but an ongoing process — one constituted through the interaction of bodily states, narrative, and relational context (Damasio, 1994; Stern, 1985). A person’s sense of themselves as an agent — as someone who does things, who makes choices, who affects the world — requires feedback from the environment: confirmation that one’s actions have the effects one intended, that one’s intentions are being received and responded to. An intent orchestration system that provides this feedback consistently and legibly is not merely serving the user’s functional needs. It is participating in the ongoing constitution of their sense of themselves as agents in the world.

Positioning: Invasive and Non-Invasive Pathways in the Neural Interface Field

Earlier drafts of this paper deliberately avoided direct comparison with other neural interface programs. The reason was modesty about a field that is moving quickly and a preference for letting the architecture argue for itself. We believe this modesty obscures rather than clarifies key points of differentiation. The strategic distinction between invasive and non-invasive neural interfaces is one of the most important variables shaping who this technology can reach, when, and at what cost — to the body and to the broader social system. The contrast deserves to be drawn plainly.

The most visible invasive program is Neuralink, whose surgically implanted intracortical arrays have produced compelling demonstrations of cursor control and communication restoration in patients with severe paralysis, including ALS. The signal density and resolution available from intracortical recording are real, and the demonstrations have done a public service in expanding the perceived scope of what neural interfaces can do. Other invasive approaches — including ECoG arrays, stentrode-style endovascular electrodes, and various intracortical research programs — sit on the same fundamental trade curve: higher fidelity signal at the cost of surgical risk, biocompatibility constraints, regulatory pathways measured in years, and per-patient costs that limit deployment to the most acute clinical populations.

Naqi’s pathway is structurally different. The platform is non-invasive, in-ear, daily-wearable, requires no clinical procedure, and can be adopted at the price point and form factor of consumer audio hardware. The signal density per channel is lower than what an intracortical array provides — this is not a contested claim — but the signal Naqi captures is sufficient for the command-class problem the platform is solving (intent classification across a structured action space), and the platform compensates for the channel-density gap with ear-canal anatomical proximity to relevant musculature, on-device personalization, multi-modal fusion across micro-gesture, EMG, and pre-motor signal correlates, and continuous orchestration logic that resolves uncertain individual classifications against a model of user context.

The strategic consequence is that the two pathways serve different parts of the addressable population, on different time horizons, under different risk profiles. Invasive systems will continue to advance the state of the art for the most severe paralysis cases for whom the surgical risk is justified by the potential restoration of function. Non-invasive systems — Naqi’s among them — can be adopted today, scaled to millions of users, deployed across the long tail of accessibility needs, and integrated into everyday life without clinical intervention. These are complementary, not competing, propositions, and the field is best served by being clear-eyed about which problems each approach is best positioned to solve.

Within the non-invasive landscape, several programs deserve naming. Wisear (recently acquired by Naqi) and IDUN Technologies are pursuing in-ear EEG and bio-signal capture for command and biometric applications. Neurable has focused on EEG-integrated headphones and attention-state detection. Meta’s Neural Band targets surface EMG for wrist-worn input to AR/VR contexts. Each of these represents a serious effort and contributes to the maturation of the broader category. Naqi’s specific differentiation is the planned combination of three properties: (1) the ear-canal form factor, which captures pre-motor, EMG, and micro-gesture signals from a single anatomical location with no head-worn or wrist-worn footprint; (2) (in-development) continuous neural command stream model rather than a discrete-trigger model, which is what makes the orchestration architecture in this paper meaningful rather than aspirational; and (3) (in-development) intent continuity layer described in the next section, which converts a stream of low-level neural and behavioral signals into coherent action across the device ecosystem rather than mapping each signal to a single device command.

None of this requires diminishing the achievements of any other program. It requires only the willingness to say what Naqi is and what Naqi is not, so that readers — clinical, technical, and investment — can assess the platform on its actual claims rather than on the implicit claims of an unmarked field.

The Intent Continuity Layer: A Technical and Ethical Architecture

We have used the term intent continuity layer throughout this paper. Here we specify what we mean with more precision.

The intent continuity layer is a persistent, personalized software architecture that mediates between the neural sensing hardware and the device ecosystem. It has five core functions.

The context engine maintains a continuously updated probabilistic model of the user’s current context — physical location, time of day, recent interaction history, active tasks, and social context. This context model is updated by combining neural signal data with environmental sensor data and historical behavioral patterns.

The intent model maintains a probability distribution over likely user intentions given the current context model. It is updated in real time as new neural signal data arrives, and it is initialized using both the user’s personal history and population-level priors from a federated learning system that preserves individual privacy while enabling collective learning.

The action planner translates the intent model’s top-ranked interpretations into specific device commands, evaluating each proposed action for reversibility, consistency with established user preferences, and potential for unintended consequences.

The ethical arbitration module applies a set of user-defined and system-defined constraints to the action planner’s recommendations, filtering out actions that would violate the user’s privacy preferences, attentional boundaries, or established behavioral patterns. This module is inspectable: the user can, at any time, review the constraints that are active and modify them.

The feedback integrator processes the user’s responses to executed actions — both explicit corrections and implicit signals such as the follow-on neural signals that immediately follow an action — and uses this information to update all upstream components.

This architecture is not a product specification. It is a framework — a set of design principles with enough specificity to guide concrete engineering decisions while remaining general enough to accommodate the diversity of user profiles, device ecosystems, and deployment contexts that Naqi’s platform will encounter. The key design decisions flagged here as requiring further research: the appropriate scope of the context engine’s environmental sensing; the methods for ensuring that the ethical arbitration module’s constraints are meaningfully inspectable by users with limited technical literacy; and the conditions under which the intent continuity layer should defer to explicit user commands rather than proactive intent inference — the conditions, in other words, under which the system should wait to be told rather than inferring.

Beyond a Single Flagship: The Long-Horizon Application Space

Discussions of neural interface technology have, for understandable reasons, gravitated to ALS as the headline use case. The disease’s progression toward locked-in states, and the demonstrated ability of neural interfaces to restore communication where speech and motor function have been lost, make for a powerful and clinically meaningful demonstration. We do not wish to diminish the value of this work. The restoration of communication for an ALS patient — even partially, even briefly — is among the most significant uses of this technology that exists.

At the same time, the clinical reality of ALS is that life expectancy after diagnosis is often measured in two to five years, and the experience of the disease is dominated as much by the management of physical decline and the preservation of dignity as by the technical problem of communication. Positioning ALS as the flagship case — the central image of what neural interfaces are for — risks two distortions. It narrows public imagination about who else this technology serves. And it can, inadvertently, frame the value of neural interfaces in terms of a use case where the time horizon for benefit is unavoidably short.

A more honest framing places ALS as one important application within a broader and longer-horizon application space. The conditions that benefit from continuous, frictionless, hands-free, voice-free, screen-free interface — and where the benefit compounds over years and decades rather than months — include: spinal cord injury (where independence support extends across an often-normal lifespan); multiple sclerosis (where fluctuating motor and fatigue states make a graceful, low-effort interface particularly valuable); stroke recovery (where assistive interface use can complement rehabilitation over many years); cerebral palsy (where lifelong assistive use is the norm); Parkinson’s disease and other movement disorders (where tremor and bradykinesia interact poorly with conventional interfaces); age-related motor decline (where the population is large and the benefit horizon is decades); and cognitive accessibility for autism, ADHD, and related profiles where the load of maintaining attention on conventional screens is itself the disabling factor.

Beyond the strictly clinical, there is an enormous workforce and daily-life population for whom hands-busy, eyes-busy, voice-inappropriate environments — surgical theaters, manufacturing floors, field service contexts, defense settings, parents managing small children, anyone in a crowded public space — make conventional interfaces friction-laden in ways that a neural earbud can simply remove. We mention this not to dilute the accessibility argument but to make explicit that the user base for non-invasive neural interfaces is structurally larger than the population of patients with any single diagnosis, and the long-horizon applications are where the technology’s compounding value is greatest.

This broadening of the use case landscape is also a strategic claim. A platform whose value proposition depends on a single flagship condition is fragile in ways that a platform serving a long tail of long-horizon applications is not. Naqi’s design choices — non-invasive, daily-wearable, consumer-pricing-compatible, integrated with mainstream device ecosystems — were made with the long tail in mind from the beginning, and the orchestration architecture described in this paper is what allows the same underlying platform to serve users at very different points along the autonomy gradient without per-condition re-engineering.

Conclusion: Building the Braided World

The term braided describes, better than any other we have found, what neural interface orchestration is creating. Human biological systems — the nervous system, the muscular system, the regulatory systems of emotion and attention — are becoming braided with machinic systems: AI agents, device ecosystems, cloud processing, federated learning. The braid is not a merger; the strands remain distinct. But they are no longer separable in the ways they were when the interface was a keyboard and the boundary between person and machine was a clear surface you touched and then withdrew from.

This braiding has been imagined in speculative fiction for decades. It is now being built into frontier models, agentic AI, and the neural interface platforms that serve as their orchestration layer. Naqi Logix is part of this building. The question this paper insists on keeping open is: what kind of braid are we making? A braid in which each strand supports the integrity and strength of the others? Or a braid in which one strand gradually dominates, captures, and substitutes for the others?

We do not know the answer yet. The technology is too new, the deployment contexts too varied, and the human stakes too high for confident predictions. What we can say is that the architecture of intent orchestration — the specific decisions being made now about how neural signals are processed, how intentions are inferred, how actions are taken, and how errors are handled — will determine which kind of braid the world gets. Those decisions must be made with the full awareness of what is at stake: not just the functional capabilities of people with neurological conditions, but the attentional sovereignty, the psychic privacy, and the relational bonds of every person who inhabits the increasingly braided world that neural interface orchestration is creating.

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ABOUT NAQI LOGIX

Naqi Logix is a neurotechnology software and AI company building the world’s most advanced non-invasive human-machine interface platform. Through its neural earbud architecture — detecting micro-gestures, facial muscle signals, and neural pre-motor activity — Naqi enables hands-free, voice-free, screen-free control of any digital device. The platform is designed from the ground up for accessibility, dignity, and human agency. Naqi has been recognized by TIME Magazine as one of the Best Inventions of 2023, received the Gold Edison Award for Social and Cultural Impact in 2024, and earned Best of Innovation honors at CES 2025 and 2026. The company holds 30+ issued patents and has secured government research contracts in assistive neural interface technology.

ABOUT THE AUTHOR

Dr. Rodney Sappington is Head of AI at Naqi Logix, Researcher at the Institute for Advanced Consciousness Studies (Santa Monica, CA), and Member of the Alfred Lee Loomis Innovation Council at the Stimson Center (Washington, DC). He holds a Ph.D. in Biomedical Informatics from Johns Hopkins University. He is co-author of arXiv:2512.17989 and Founder of Acesio Inc. His research spans neural signal processing, machine consciousness, AI alignment, agent-based systems, and the psychoanalytic and social dimensions of human-machine interaction.

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