Straight answers on mimOE™, mimOE Studio and its Agentix Suite.

mimOE is a purpose-built Agentix-Native operating engine. A 10 to 20 MB cross-platform engine that sits on top of any operating system and powers the agent layer. mimOE runs on phones, sensors, cameras, edge gateways, servers, and the cloud. It supports any CPU, GPU, or NPU. It runs on Linux-based systems, Windows, macOS, Android, iOS, QNX, Raspbian, and cloud environments. It is optimized for CUDA, CUDA Container, ROCm, Vulkan, and SYCL. It has a built-in API and MCP gateways with 3 built-in AI runtimes to optimally execute any combination of AI models: large, small, multimodal, domain-specific, predictive, and generative. Because not all Agentix-Native workloads require GPUs, mimOE intelligently distributes inference across CPU and GPU, based on types of workloads. It operates over broadband, 5G, satellite, ethernet, or no network at all.

The moment mimOE is installed on a device, that device becomes a first-class node in an Agentix-Native infrastructure: resilient by architecture, governed by policy, and discoverable across the mesh. It offers built-in military-grade security and enables Sovereignty in Execution across five dimensions. mimOE enables agents to compute, network, and execute intelligently with zero-touch config. All mimOE capabilities are available via CLI.

mimOE decouples agent logic from hardware, operating system, network, cloud, and language model choice. That decoupling is what enables mimik's operational guarantee: Build, Execute, Operate, and Scale with Certainty.

An Agentix Operating Engine. mimOE is inference execution + agent execution + workflow operations, end to end. A runtime covers only the first of the three.

The difference is scope:

• An AI runtime executes inference: model and prompt in, tokens or predictions out. llama.cpp, ONNX Runtime, and LiteRT are runtimes.
• An operating engine executes and operates the end-to-end workflow: inference execution + agent execution + workflow operations (identity and authorization, policy enforcement, discovery, routing, scheduling, lifecycle management, observability, mesh participation).
• Whether inference runs on a built-in runtime, a third-party inference server, or a third-party AI model, the trace, the policy, and the workflow stay in mimOE.

• mimOE executes inference through three built-in AI runtimes, llama.cpp, ONNX, and LiteRT, selecting the optimum one per device and model.
• mimOE also makes third-party runtimes and inference servers, such as Intel OpenVINO (OVMS), NVIDIA NIM, Ollama, and others, and third-party AI models, discoverable across the mesh.
• An agent calls one API and never selects an inference provider; agent logic and inference provider choice are separated.
• Routing configuration is set dynamically through discovery, the governance layer sets which inference provider each workflow uses, and mimOE's intelligent routing directs each job to the corresponding built-in runtime, third-party inference server, or third-party AI model.
• The runtime layer is interchangeable, built-in or third-party; the operating engine around it is the product.

DFC-AI™ is a completely new architectural paradigm that enables agents to compute, network, and execute intelligently, with zero-trust security and zero-touch configuration.

It starts at the endpoint device, where data is born, context is closest to reality, and action must be taken. From there it extends seamlessly across a continuum to other devices, gateways, on-premises infrastructure, and multi-cloud as per workflow needs and operator choice. The endpoint is the architectural starting point, not the boundary. The continuum is the full reach. The operator decides which part of the continuum any given workload uses, on what hardware, in what geography, under what policy.

This is not Edge Computing. Edge assumes a hub-and-spoke relationship to the cloud, with the cloud as the brain and the edge as a forward node. Device-First Continuum has no hub. Devices, gateways, on-premises, and cloud are all execution surfaces. Connectivity is an option the operator exercises, not a precondition for the system to function.

It covers compute and AI together, not AI alone. The same architectural model applies to the underlying compute fabric and to the intelligence running on top of it. Agents discover each other, collaborate, exchange value, and act, all on the same continuum that the compute itself spans.

The result is a system that operates with structural advantages on every transaction: speed because inference is local, privacy because data does not leave the operator's perimeter, cost because cloud calls are optional, verifiability because execution is observable at the point of action, sovereignty because the operator owns where intelligence runs, and offline capability because the device is the starting point, not a degraded mode of the cloud.

mimOE has run in production with strategic partners, delivering measurable business outcomes to enterprises, including in their air-gapped operating fields. Today's launch opens it to developers, builders, and operators with mimOE Studio as the visual control and management layer.

The market is moving from generative AI to Agentix-Native systems (aka Agentic AI), and that shift changes what AI infrastructure has to do. Generative AI was a question of model and GPU. Agentix-Native is a different problem entirely: dynamic workflows where agents, mixed AI models (generative and predictive), and traditional workloads follow, observe, respond, and learn in a continuous loop, end-to-end at scale. Each workflow chooses its path based on the task, the system state, and the expected outcome.

That requires more than model and GPU. It requires both CPU and GPU intelligently distributed, networking that survives intermittent connectivity, identity and policy enforced at the device, and deployment operations that span heterogeneous hardware in the real world.

The invitation is direct: build, execute, operate, and scale Agentix-Native systems with certainty, in your agent framework of choice, on your terms.

Developers continue to write code in their IDE of choice and point inference calls at a mimOE Operating Engine URL. mimOE Studio is the visual control and management interface, built on mimOE's APIs: an Agentix-Native Workstation to view and interact with mimOE's exposed capabilities and what's operating on it. Because Studio sits on top of mimOE's APIs, teams can also build their own interfaces, dashboards, or operator consoles tailored to their workflows.

Where Windows Explorer, macOS Finder, SharePoint, and Google Drive let users navigate files across local and cloud storage, at first glance, mimOE Studio lets them navigate AI models, agents, images, traces, and mesh activity, on a single device and across the continuum of nodes. Other computing platforms appear in Studio as naturally as a printer appears on your home network, with zero networking configuration.

Users work with mimOE Studio across two phases of an Agentix-Native system's lifecycle:

During build

Developers upload AI models to local or remote nodes, start them, discover other models and agents running in the mesh, interact with any running model or agent (including chat) to validate behavior instantly, and trace every call, including inputs, outputs, tokens, and routing decisions, live, from anywhere to anywhere. Builders can also benchmark each workload across CPU and GPU to find the most efficient execution path. Agentix-Native systems require both, not just one. In under five minutes from download, a developer has a live inference environment, a running model, and a chat interface to infer, test, measure, and view results. All with no cloud account, no setup cost, no token cost.

During execution

mimOE runs inference and agent workflows on the device and across the continuum. This gives agents the ability to compute, network, and execute intelligently with zero-touch config.

mimOE Studio is designed for everyone involved in operationalizing Agentix-Native systems and right during the build of systems:

• Builders: application developers, AI engineers, engineering managers, and DevOps teams building Agentix-Native systems across all computing form factors, device-first but continuum, including offline-first operations. Studio is their visual workstation, providing a live inference environment, model and agent discovery, CPU and GPU benchmarking, and behavior validation before deployment.
• Operators: platform engineers, enterprise architects, and OT managers. Studio gives them mesh-wide visibility, routing observability, and live workload management across the Device-First Continuum.
• System Integrators: consulting firms and solutions partners designing, deploying, and integrating Agentix-Native systems for enterprises. Studio gives them interoperability across business units and with legacy and new systems as they get built.
• IT and Finance Leadership: CIOs, IT executives, and CFOs. Studio surfaces compute utilization, built-in telemetry, information exchange for privacy and security, and the cost picture of Agentix-Native systems across the continuum.
• Device OEMs and Silicon Vendors: Studio is an Agentix-Native Explorer they can ship with their devices, turning their hardware into a ready-to-use Agentix-Native platform for builders out of the box.

mimOE Studio supports the full development lifecycle for Agentix-Native systems. With Studio, developers can:

• Discover the mesh of mimOE nodes across Local Network, Account, Proximity, and Multi-cloud, and interact with any node from a single workstation.
• Load, unload, and switch AI models on demand between My Device and Network, with seamless CPU and GPU benchmarking to choose the most efficient execution path.
• Validate model and agent behavior live through chat or direct API before any deployment.
• Trace every API call end-to-end across the mesh with full visibility into routing decisions, latency, inputs, outputs, and token usage.
• See the live runtime picture in real time: uptime, requests served, data in and out, and token usage across all running workloads.
• Inspect the API surface for every model and agent: every workload exposes both a chat interface and a direct API endpoint, ready to plug into any IDE or CI/CD pipeline.

For mimOE Studio, cross-platform means two distinct things:

mimOE Studio is available on Android and Apple app stores, macOS, and Windows. Core concepts including projects, agents, runtimes, deployments, and device profiles are consistent across all platforms, so a user can switch devices without re-learning fundamentals.

The mimOE Operating Engine executes the workloads developers build and validate in Studio. It runs across Linux, Windows, macOS, Android, iOS, QNX, Raspbian, and cloud environments, on CPU and GPU. A developer working in Studio can discover, interact with, and operate workloads running on a Raspberry Pi, an NVIDIA Jetson, an iOS device, or a cloud node from the same workstation.

mimOE Studio runs on macOS, Windows, iOS, and Android.

• On macOS and Windows, Studio provides a File Explorer-like environment for navigating mims, AI models, agents, discovery, and traces. The two desktop platforms are functionally equivalent, with differences only in OS-level integrations (system menus, file dialogs, native notifications).
• On iOS and Android, Studio is optimized for monitoring and field validation: discovering nodes, viewing live runtime metrics, interacting with running models and agents, and tracing activity from anywhere.

Yes. mimOE Studio is generally available today. Developers can download it from the Apple App Store and Google Play for mobile, and from the mimik developer portal at developer.mimik.com for macOS and Windows. Enterprise licensing and team plans are also available starting at launch.

No. Many teams standardize on Windows or macOS for development and use a small set of iOS or Android devices as representative runtime targets for testing and demos. Cross-platform support exists so organizations can choose a mix that matches their device strategy and workforce tools.

mimOE enables agents to compute, network, and execute intelligently across the Continuum. At a high level, the capabilities are:

Compute

• Cross-platform Operating Engine: a 10 to 20 MB engine that runs on Linux, Windows, macOS, Android, iOS, QNX, Raspbian, and cloud environments
• Intelligent CPU and GPU distribution: workloads routed to the most efficient execution path based on type
• Three built-in AI runtimes: handle any combination of large, small, multimodal, domain-specific, predictive, and generative models
• Acceleration support: optimized for CUDA, CUDA Container, ROCm, Vulkan, and SYCL
• Traceable execution: every inference decision logged (model selected, device used, CPU or GPU, latency)

Networking

• Built-in API gateway: every node exposes its capabilities through standard APIs
• Built-in MCP gateway: native Model Context Protocol support for agent-to-agent communication
• Mesh of nodes: every mimOE installation becomes a discoverable node across the Device-First Continuum
• Offline-first operation: works fully without connectivity, and equally over broadband, 5G, satellite, or ethernet
• End-to-end traceability: every API call across the mesh fully traceable, including routing decisions and timing

Execute

• Sovereignty in Execution: enforced across five dimensions
• Built-in military-grade security: zero-trust by default
• Policy-governed workloads: every action runs under explicit policy
• Zero-touch configuration: nodes activate, authenticate, and discover each other automatically
• Built-in telemetry: live runtime metrics for every workload (uptime, requests, data flow, tokens)
• Traceable policy and lifecycle: every authorization, security event, and lifecycle action logged

mimOE runs AI workloads natively on each device where it operates. Inference happens where data lives, with no need to send raw data elsewhere. Three built-in AI runtimes handle any combination of large, small, multimodal, domain-specific, predictive, and generative models. The optimum runtime is selected based on the target device and the AI model's needs, with workloads intelligently distributed across CPU and GPU. Agentix-Native systems require both, not just one.

Every model running on mimOE exposes a standard API endpoint, making it interchangeable from the application's perspective with any cloud-hosted model. Authorized devices also participate in a mesh across the Device-First Continuum, so workloads can span devices, gateways, on-premises, or cloud as per workflow need based on context and/or operator choice.

This architecture is particularly valuable for industries such as healthcare, manufacturing, finance, and public safety, where data sensitivity and response time requirements make cloud-only AI impractical.

mimOE's security is parametric. It is continuous, contextual, and enforced at every transaction, not a gate you pass once. Every agent is the combination of app logic and the mimOE Operating Engine, so identity and authorization travel with the agent wherever it runs.

mimOE's security operates through six built-in capabilities, captured on every transaction:

• Per-transaction authentication: every mimOE-to-mimOE exchange is authenticated independently, with no session tokens to harvest or replay
• Rich metadata capture: identity, device, peers, timing, and channel are recorded for every call, building a full audit trail
• Context verification: agents know their peers, so authorization accounts for who is talking to whom and in what context
• Policy enforcement: channel-level access is governed by mimOE; content-aware rules run inside each agent's app logic
• Real-time revocation: channels can be terminated instantly across the mesh, with peers notified on-device and no cloud round-trip
• Full audit ledger: every transaction is logged. The ledger can run locally per agent for low-latency, air-gapped operation, or be aggregated centrally for cross-agent pattern detection and SOC compliance. Same metadata, different deployment.

Centralized management for policy, config, and provisioning is supported, but execution and enforcement always happen locally on the node. This is what enables Sovereignty in Execution across five dimensions: agents act under their own authority, with cryptographic identity, while remaining fully accountable through the ledger.

Yes. Offline-first operation is a defining capability of the mimOE platform, not a fallback mode. Agentix Systems can use mimOE to discover agents and nodes across the local mesh, execute AI inference, communicate node-to-node, and maintain state locally without any network connection. The same systems operate equally over broadband, 5G, satellite, or ethernet when connectivity is available. When connectivity returns or expands, synchronization happens under explicit operator policy, in whichever direction the workflow requires.

Traditional cloud AI architectures route all data to a central cloud, perform inference there, and send results back to the device. Devices, despite their powerful compute, act as thin clients waiting for results. This creates dependency on network availability, adds latency, and centralizes sensitive data in ways that create compliance and privacy challenges.

mimOE inverts this model. It is purpose built for the agentix paradigm vs. the traditional cloud paradigm. Processing happens where data is born, context is closest to reality, and action must be taken. The cloud remains available for global functions, extra-large processing, large model training, and global visibility, but it is not required in the critical path. This gives solutions the benefits of AI without the constraints of cloud-only designs.

The operator decides which part of the continuum each workload uses, on what hardware, under what policy.

Four structural differences

• mimOE works equally well online or offline. Cloud architectures require a network round-trip.
• mimOE keeps data and computation under operator control through Sovereignty in Execution across five dimensions. Cloud architectures centralize both.
• mimOE provides full-stack Agentix-Native infrastructure and lifecycle management. Cloud architectures stop at CI/CD.
• mimOE exposes two interfaces for workloads and agents: standard API endpoints that are interchangeable with any cloud-hosted model, and a built-in MCP gateway that makes their functions discoverable to other agents and services on device and across the continuum. Solutions can seamlessly operate between cloud and device execution with no code changes.

Yes. You define agents and policies once, package them as mims, then deploy across device classes including x86 Windows platforms, ARM Android and iOS devices, macOS workstations, Linux-based systems from Raspberry Pi and NVIDIA Jetson boards up to industrial PCs and servers, QNX-based safety-critical systems, and cloud nodes, through configuration and deployment profiles instead of maintaining separate codebases. The mimOE Operating Engine handles platform differences, including CPU and GPU distribution.

Both paths work. The difference is what the agent code has to carry.

Written to run on mimOE

• Networking, API gateway, MCP gateway, zero-trust security, discovery, and routing are built into mimOE, so none of it is developed inside the agent logic.
• Lighter agent code means faster logic, processing, and exception handling, and better memory, CPU, and GPU efficiency.
• Wherever mimOE runs, the agent runs, with no code change and no porting.

Linking an existing agent

• Where the agent sits: side by side with mimOE on the same machine or on a different machine; all it needs to link is the mimOE URL and a security key.
• With no code change: the agent can discover computing nodes, use intelligent routing as a service, and also link with mimOE's MCP gateway to expose itself via MCP and use tools served through the gateway, such as search tools.
• With a small, one-time code change: the agent uses the API gateway.
• The trade-off: cross-platform benefits and other capabilities that mimOE provides, such as networking, zero-trust security, and many others, still have to be developed inside the agent's own code, so the code is heavier, and that code changes each time the agent moves from one platform to another.

Models and compute get a system to PoC. Production fails at the operationalization layer, where the same barriers repeat across industries:

• Cloud dependency: AI requiring cloud connectivity cannot reach remote, mobile, or air-gapped operations
• Centralized orchestration: distributed fleets without local coordination create latency, cost, and single points of failure
• Sovereignty gap: platforms unable to enforce execution-level sovereignty are disqualified from regulated procurement
• Architecture fragmentation: bespoke per-platform rewrites make updates slow, expensive, and risky
• Infrastructure failure liability: data center dependency turns any outage into an operational shutdown
• Cost and energy at scale: cloud backhaul makes fleet-scale operation economically unsustainable
• Vendor lock-in or security exposure: mixed environments without a unified security model force a choice between the two

mimOE removes these barriers at the device level.

• Devices powered by mimOE operate as self-sufficient nodes during outage, disaster, or cyber attack.
• Agents continue to execute across unreliable networks, remote sites, and fully air-gapped environments.
• Connectivity is an option the operator exercises, not a precondition for the system to function.

Agentix Benchmarking, powered by mimOE, is a new CI/CD-class tool for operationalizing Agentix-Native systems. An oscilloscope for agent workflows: it runs your agent fleet for real on your target compute and measures what happens, instead of calculating what should happen.

Two questions decide the hardware budget:

• Headroom on target compute: how many agents will this hardware run before CPU, GPU, or memory becomes the limit
• The AI model for the workflow: which form of model each step needs (LLM, SLM, narrow, or application-specific) instead of defaulting to a general LLM

Today the first is answered with a spreadsheet, the second by habit. Both fail in production because the calculator never ran the work.

Three profiles, one instrument:

• Capacity (shipping in this release): how many agents the hardware holds, where CPU, GPU, and memory run out of headroom, verified completion, the full trace, cost and latency
• Model matching (being finalized): the smallest model that still verifies each step of the workflow
• Mesh capacity (upcoming release): the same fleet measured as one workload across the nodes of the Device-First Continuum

Real. Every agent executes real tools on a real mimOE Operating Engine.

• Work is verified against the expected result, not the agent's own report.
• Every agent is inspectable down to raw prompt and output.
• Checkable from outside: open any system monitor next to the run and watch the same duty cycle independently.

From an actual run on one mimOE box: 80 of 80 agents verified, 0 failed, real Qwen3.6-35B model, GPU width 4, mimOE memory flat at 96 MB.

• Developers and AI engineers: find out before shipping how many agents a box holds, what breaks first, and which operation to fix. Run the built-in reference workflows for a first result in minutes, then sweep to find your ceiling.
• Silicon and SoC vendors: customers ask how many real agents a part will run, and a teraflops figure cannot answer it. Run it on reference boards and ship it alongside the silicon as a proof instrument; the number is generated by the customer on their own bench.
• OEMs and device makers: a buyer trusts a number they generated themselves more than one printed in a brochure. Ship it as an evaluation instrument so prospects size their fleet on the hardware they are holding.
• Enterprise IT and platform teams: move ceiling discovery from production to procurement. Benchmark candidate workloads on candidate hardware before committing budget; the run never leaves your infrastructure.

Each answers a different question, built from the app-native and cloud-native view:

• Endpoint load tools (NVIDIA AIPerf, Red Hat GuideLLM): fire requests and time responses, no engine underneath, no verification. Agentix Benchmarking runs the whole agent loop on a real engine with tools actually executed.
• Hardware leaderboards (AA-AgentPerf): rank accelerators in someone else's datacenter with simulated tool calls. Agentix Benchmarking produces a number you generate on your own compute.
• Request-level benchmarks (MLPerf Inference, MLPerf Client): serving throughput and single-stream tasks, no agent loop, no fleet. Agentix Benchmarking measures verified agent fleets.
• Sizing calculators: predict capacity on paper from weights and cache. Agentix Benchmarking measures it by running real verified agents.
• Observability and tracing: watch single calls in production after the fact. Agentix Benchmarking operates at fleet scale before the compute is committed, and separates operationalization from inference.

Running the benchmark installs and operationalizes mimOE, the Agentix Operating Engine, on your target compute. The download is one integrated tool, three panes on one engine:

• mimOE Studio: visibility and control of live nodes
• mimik Agent Harness: building and running the agent workflow
• Agentix Benchmarking: measuring fleet capacity

Because the engine is constant, the same workflow produces comparable numbers on a laptop, a gateway, an industrial board, or a cloud node. The capacity you measure on a box is the capacity that box carries in production. Testing and production share one system of record.

• Download the tool and point it at the hardware you run today or the hardware you are evaluating.
• It ships with reference Agentix-Native workflows built on the mimik Agent Harness; first verified result in minutes.
• The run stays entirely on your hardware, and nothing leaves it.

Developers use mimOE Studio as their Agentix-Native Workstation. They upload AI models to local or remote mimOE nodes, package logic as mims, and discover other models and agents running in the mesh. They interact with any running model or agent live, through chat or direct API, to validate behavior; benchmark workloads across CPU and GPU to find the most efficient execution path; and trace every call end-to-end with full visibility into routing, latency, inputs, outputs, and tokens. They then deploy mims across the Device-First Continuum (x86 Windows platforms, ARM Android and iOS devices, macOS workstations, Linux-based systems from Raspberry Pi and NVIDIA Jetson up to industrial PCs and servers, QNX-based safety-critical systems, and cloud nodes) through configuration and deployment profiles. The mimOE Operating Engine handles platform differences automatically.

Note: a mim is a micro intelligent module that is developed in the form of microservices.

The iOS and Android apps give developers a live mimOE node on their own phone or tablet, so they can deploy builds directly from Studio, inspect performance, and test on real sensors and networks in the field. This closes the loop between desktop simulation and real-world runtime behavior on devices.

Any Full-Stack Developer. Teams with experience in microservices, APIs, and native mobile application development can ramp quickly, since mimOE Studio builds on familiar concepts like services, events, and policies mapped onto runtimes. Existing mobile, embedded, or cloud engineers can specialize into device-native roles by learning the Studio runtime and deployment model. Additional higher-level libraries, platform SDKs, and plugins are provided on GitHub to simplify integrating mimOE into even existing applications.

It operationalizes the hardware for Agentix-Native systems. It turns hardware into Agentix hardware, ready to operate Agentix-Native systems out of the box.

mimOE is an Agentix Operating Engine. Think of it as a chip in software form: it operationalizes the OEM's device for Agentix-Native systems the moment it ships, exposes the device's capabilities and functions through standard APIs and an MCP gateway, and makes the device immediately consumable, discoverable, and even monetizable for the owner as Agentix hardware.

Like a hardware chip, mimOE adds a defined set of capabilities to the device: AI inference distributed across CPU and GPU, mesh participation, Sovereignty in Execution, identity and authorization, observability, and policy-governed lifecycle. Unlike a hardware chip, it ships as software, updates over the air, and can be tuned to the OEM's specific silicon and form factor without a board revision.

The practical effect: OEMs do not have to build the operating layer that makes their hardware Agentix-capable. mimOE is that layer.

Hardware that ships with mimOE pre-loaded. The device is no longer general-purpose hardware waiting to be configured:

• It discovers, coordinates, and executes agent workflows from the moment it is deployed.
• It operates under any connectivity condition and within any sovereignty constraint the customer requires.
• The distinction: AI-capable hardware can run models; Agentix-Native hardware is AI-operational from day one.

• The buying conversation shifts from AI-capable to AI-operational; the product wins on fit, not specs.
• Enterprises activate on day one instead of spending 12 to 18 months building operationalization infrastructure.
• Device capabilities become agent microservices, opening a recurring software and services layer on every unit sold.
• Agentix-Native hardware commands a premium; the OEM sells a deployment foundation, not compute.
• The solution works consistently across chip vendors, cloud providers, and AI model platforms, with no re-platforming risk.

By pre-loading the mimOE Operating Engine and Studio, OEMs ship hardware that is a ready-to-use sovereign Agentix-Native platform out of the box. Devices arrive at the customer already able to run AI inference, host agents, participate in a mesh, and expose APIs and MCP endpoints with no further integration work.

What pre-load looks like depends on the device class:

• Devices with a screen (phones, tablets, laptops, screened embedded systems): both the mimOE Operating Engine and mimOE Studio ship on the device. Users get a native file-explorer-like experience for AI models, agents, mims, and runtime telemetry directly on the device.
• Headless devices (gateways, robots, sensors, industrial controllers, vehicles): the mimOE Operating Engine ships on the device. mimOE Studio on a developer or operator's desktop or mobile device interacts with the headless device through the mesh, giving the same visibility and control as if Studio were running locally.

OEMs use mimOE Studio to define reference mims, pre-install the mimOE Operating Engine tuned for their hardware, certify their devices against latency, privacy, and interoperability requirements for AI workloads, and publish deployment profiles that developers select when targeting that OEM's hardware.

The result: developers can target the device on day one, enterprise buyers get Sovereignty in Execution embedded in the hardware, and the OEM gains a path to recurring revenue as agent workloads execute on their hardware over its operating lifetime.

Note: OEMs that want a branded or hardware-specific experience can build their own interface on top of the mimOE API, the same APIs Studio uses. This lets the OEM ship a custom Agentix-Native workstation matched to their product UX and brand, while keeping the underlying operating engine, mesh, and Sovereignty in Execution capabilities of mimOE intact.

Windows and macOS support lets OEM engineering teams run mimOE Studio alongside their existing firmware, driver, and hardware validation tools on standard laptops or workstations. Multiple mimOE instances can run concurrently on a single machine as independent live nodes, forming a real multi-node fleet without leaving the desktop. From any node in that fleet, engineers can trigger workflows that propagate across the others, validate mesh behavior at scale, run automated CI/CD tests against live mimOE nodes, and exercise hardware-specific runtime features such as accelerators, secure enclaves, or NPUs. This compresses the test, performance measurement, and operations cycle from hours of physical-lab setup to minutes on a developer workstation.

For OEMs building phones, tablets, or rugged handhelds, the iOS and Android availability of both the mimOE Operating Engine and mimOE Studio means their devices ship as full Agentix-Native workstations. The operating engine and the visual interface live on the same form factor, not dependent on a separate desktop to be useful.

OEM teams use this to validate the pre-installed mimOE Operating Engine on real hardware with real sensors and real networks, test reference mims and confirm Agentix-Native workloads behave correctly on their specific silicon, and demonstrate live capability to partners, developers, and customers directly on the device with no custom demo code required.

For phone and tablet OEMs competing in the AI-on-device category, this is differentiation buyers can see and interact with on the product they are evaluating.

System integrators use mimOE Studio to design, build, and operate Agentix-Native solutions for enterprise customers. They model customer-specific workflows, place mims and agents on the appropriate nodes across the Device-First Continuum, and manage deployment pipelines across mixed Windows, macOS, Linux, iOS, Android, QNX, embedded, and cloud fleets.

Three things SIs offer enterprises with mimOE:

• Architectural fit, not replacement. Agentix-Native is an enhancement to the microservices pattern enterprises already run everywhere. AI models become components inside that pattern, not a parallel stack to migrate to.
• Legacy bridge without rewrite. The mimOE APIs and MCP gateway expose Agentix capabilities to existing enterprise applications, giving interoperability across customer business units and with legacy systems on day one.
• Iterative rollout. SIs deliver AI adoption to enterprises in phases instead of big-bang, build reusable solution patterns across customers, and accelerate enterprise rollout without wholesale rebuild.

The result for their enterprise customers: peace of mind to adopt AI in phases, certainty as the system scales, faster revenue from new AI capabilities, and a future-ready architecture that preserves existing investment rather than displaces it.

On Windows and macOS, SIs work from a single desktop workstation that gives them visual access to multiple customer mimOE deployments. They view live mesh topology and workload distribution across on-premises, device, and cloud nodes, configure customer-specific policies for multi-tenancy, access control, and observability, and test changes against live multi-node fleets before they touch a customer's production environment.

For SI field engineers, iOS and Android phones and tablets are live mimOE nodes they carry to customer sites. On arrival, the device joins the customer's mesh, runs real agents and AI workloads, and lets the engineer validate behavior against the customer's actual network, sensors, and operating conditions. This compresses pilot and proof-of-concept cycles, supports live customer demos and on-site troubleshooting, and lets SIs prove value at the customer's site without procuring or shipping specialized hardware.

It operationalizes the silicon for Agentix-Native systems. It turns the chip into Agentix-on-Chip, making every device built on it Agentix hardware out of the box.

mimOE is an Agentix Operating Engine. Think of it as a chip in software form, pre-integrated at the silicon level (Agentix-on-Chip): it operationalizes the chip for Agentix-Native systems, exposes the silicon's capabilities and functions through standard APIs and an MCP gateway, and makes every device built on the chip immediately consumable, discoverable, and monetizable as Agentix hardware.

mimOE adds a software layer to the silicon that delivers AI inference distributed across CPU, GPU, and NPU resources, mesh participation, Sovereignty in Execution, identity and authorization, observability, and policy-governed lifecycle. Pre-integrated at the chip level, it ships as part of the silicon's value proposition, so OEM customers receive a chip that operationalizes their device for Agentix-Native systems on day one, without OEM-side operating-layer engineering.

The practical effect: every device built on the chip ships Agentix-capable out of the box, the chip vendor's OEM customers reach production faster, and every shipped device becomes a permanent node in the Agentix economy, expanding the chip vendor's silicon footprint with every unit.

Through pre-integration with mimOE at the chip level (Agentix-on-Chip):

• Hardware features ship as part of the silicon. Chip vendors plug their acceleration libraries, custom operators, and telemetry into the mimOE Operating Engine that ships with the chip.
• Capabilities are exposed through mimOE on day one. Those silicon-specific capabilities become addressable through mimOE's standard APIs and MCP gateway the moment the device powers on.
• Reachable from any authorized mimOE Studio node. Engineers, developers, or operators running mimOE Studio anywhere in the mesh can discover, benchmark, and trace how models and agents behave on the chip, with sovereignty and identity preserved.

OEM customers receive a chip that operationalizes their device for Agentix-Native systems on day one, without OEM-side operating-layer engineering.

Windows and macOS versions of mimOE Studio give chip vendors a standard desktop environment to publish SDK extensions, reference projects, and reproducible benchmarks for OEM customers and their downstream developers.

The same environment lets them validate silicon behavior on multi-node test fleets and stage performance comparisons against competing silicon under identical mimOE conditions.

For chip vendors powering mobile SoCs, iOS and Android availability of the mimOE Operating Engine and Studio does two things:

• Before launch: real-device measurement. Chip vendors measure silicon performance directly on real consumer devices (battery draw, thermal behavior, on-device inference latency) under the same agents and workloads they use on desktop Studio. This compresses the path from lab claims to real-device benchmarks they can publish to OEM customers and downstream developers.
• After deployment: continuous analytics. mimOE captures live execution telemetry on every authorized device in the field and feeds it back to chip vendors as continuous analytics. Like analytics SDKs developers embed in applications: instead of seeing how an app performs across users, the chip vendor sees how their silicon performs across the installed base, under real workloads, in real conditions.

You can sign up for mimOE Studio through the mimik developer portal at developer.mimik.com. Individual developer accounts are available immediately. Enterprise and team plans include onboarding support, dedicated environments, and SLA commitments.

Yes. mimik offers a free developer tier for mimOE Studio that includes access to the core tooling, a development-grade mimOE license, and documentation and sample projects. The free tier is designed to let you build and evaluate the platform before committing to a paid plan.

At launch, mimik is providing:

• Full API and SDK documentation through the developer portal
• Quickstart guides and sample applications for common use cases
• Video walkthroughs and architecture reference materials
• Community forums and a developer Slack workspace
• Enterprise support plans with direct engineering access for production deployments

mimOE is language-agnostic. mims are micro intelligence modules developed in the form of microservices. Developers can build these microservices using any language that compiles to an OCI-compatible format for portable lifecycle management. mimik provides higher-level SDKs and libraries to further simplify development for mobile native and full-stack developers.

Studio can export deployment artifacts and configuration bundles that plug into standard CI/CD pipelines. It can also be scripted or called from automation servers to push updates to Windows, macOS, iOS, Android, Linux, QNX, and cloud nodes across the Device-First Continuum for all form factors.

It gives certainty and peace of mind to enterprises. mimOE is the operating engine for enterprises to adopt, transition to, and roll out Agentix-Native (aka Agentic AI) and AI with certainty. It is the missing operational guarantee that lets enterprises Build, Execute, Operate, and Scale these systems without the pilot-to-production failure modes that have stalled enterprise AI to date.

That certainty rests on six architectural properties:

• Flexibility. Control without lock-in at any layer (LLM, OS, hardware, network, cloud). Runs on any compute, any OS, across the Device-First Continuum. The enterprise chooses where intelligence executes, on what hardware, under what policy.
• Interoperability. Heterogeneous estates integrate rather than get displaced. Legacy systems (SAP, Maximo, AS400, OT infrastructure) bridge into the Agentix-Native stack via mimOE APIs and MCP gateway, no rewrite required.
• Discoverability. Authorized compute, agents, and AI models discover one another across the mesh with zero-touch configuration. During development and testing, new nodes and workloads become visible immediately to authorized principals. During production, agents dynamically locate the right resources based on policy and availability, with no static configs that break when topology changes.
• Decoupling. Data, models, and execution decoupled from underlying infrastructure. The enterprise owns the execution layer, not just the data and the models, so AI investment compounds into durable competitive advantage instead of recurring rent.
• Cost economics. Local inference at zero marginal cost. No cloud per-token bills during dev, test, and iteration. Up to 80% reduction in cloud infrastructure costs at production scale. Energy efficiency built into the operating engine.
• Observability at every layer. Built-in visibility, traces, and audit trails at the operating engine, mesh, and agent layers, supporting compliance, troubleshooting, and ROI measurement.

What this means by role

• CEO. Own your AI future. Scale without limits. Sovereign by design.
• CIO / CTO. Production-ready AI at scale. Up to 80% cost reduction. No lock-in.
• CFO. Cost predictability and measurable outcomes. AI investment becomes platform assets that compound, not recurring infrastructure rent.
• IT and Operations leadership. One execution fabric across heterogeneous infrastructure. Resilient, observable, secure by default. Operates regardless of connectivity.

mimOE Studio is commonly licensed per seat for developers and architects, with additional tiers or bundles for OEMs, SIs, and chipset vendors depending on the number of devices, nodes, or customers supported.

Yes. mimik has an active partner program for technology vendors, system integrators, and OEM device manufacturers who want to build on or bundle the mimOE platform. Partners gain early access to platform releases, co-development support, and go-to-market resources. Inquiries can be directed to alliances@mimik.com.

mimOE is essential for any organization adopting AI or transitioning to AI from cloud-native solutions. It enables them to build, execute, operate, and scale Agentix-Native rollouts to grow the business with flexibility, certainty, and cost control. Priority verticals at launch:

• Manufacturing: real-time quality control, predictive maintenance, and process automation across the factory floor, edge gateways, and supervisory systems.
• Healthcare: on-device diagnostic AI, patient monitoring, and clinical decision support across the hospital network, with Sovereignty in Execution that goes beyond data residency.
• Transportation and logistics: autonomous coordination, fleet operations, and intelligent routing across vehicles, depots, and operations centers.
• Retail: personalization, inventory intelligence, and frictionless operations across stores, distribution centers, and supply chains.
• Smart buildings: energy optimization, occupancy intelligence, and predictive maintenance across building management, HVAC, and access systems.
• Physical AI: robotics, autonomous systems, and embodied intelligence requiring sub-second response across heterogeneous hardware.

mimOE is not a replacement for these runtimes; it builds on them. llama.cpp, ONNX Runtime, and OpenVINO are inference engines: they take a model and produce tokens or predictions. mimOE is the Agentix Operating Engine above that layer, responsible for hosting agents and mims (micro intelligence modules developed as microservices) on the device, routing inference requests to the appropriate engine, exposing OpenAI-compatible APIs to applications, managing identity and access under Sovereignty in Execution, and interacting with peer devices across a programmable mesh. A typical mimOE node uses llama.cpp or ONNX Runtime under the hood for execution and lets developers compose, secure, and distribute the resulting capability across the Agentix-Native infrastructure. The runtime layer is interchangeable; the operating engine around it is the product.

Existing edge platforms generally extend cloud-native orchestration models down to gateway-class hardware. They assume a control plane in the cloud, container-based workloads, and a fleet centrally managed from a single tenant. mimOE was designed for the Device-First Continuum: every node is a peer with its own identity under Sovereignty in Execution, capable of operating, discovering services, and routing inference locally without a cloud control plane in the critical path. The mimOE Mesh does not require a broker, and nodes can form transient or persistent topologies across mobile, desktop, and embedded form factors that traditional orchestration platforms either do not reach or treat as second-tier. mimOE is complementary in environments where Kubernetes already runs at the gateway, and substitutive where the deployment target is a phone, tablet, or embedded device that cannot host a Kubernetes node.

Platform-native AI stacks expose hardware-accelerated inference inside a single operating system or silicon family. They are powerful but siloed: an application built on Apple Foundation Models does not run on Android, and a Qualcomm-tuned pipeline does not port to an Intel laptop. mimOE provides a cross-OS, cross-silicon Agentix Operating Engine that calls into these native stacks where they exist and falls back to portable runtimes where they don't. The result is a single application that runs across iOS, Android, Windows, macOS, Linux, and embedded Linux, while still benefiting NPU, GPU, or accelerator paths on each platform. mimOE is the operating engine that makes platform-native AI usable across heterogeneous Agentix-Native infrastructure.

Agent frameworks focus on orchestrating model calls, tools, and prompts within a single process, usually running in the cloud or on a developer's laptop. They do not address how agents are deployed, identified, isolated, or coordinated across devices, and they typically assume reliable network connectivity to a hosted model. mimOE addresses the operational and infrastructure layer: where the agent runs, how it is identified under Sovereignty in Execution, how it interacts with other agents, and how it executes when offline. mimOE agents can expose and consume MCP tools natively, so existing MCP ecosystems remain interoperable. The Agentix Operating Engine adds the device-side hosting, mesh routing, and trust model that pure-framework approaches don't provide.