mimik Launches mimOE Studio to Accelerate Agentix AI Operations with Sustainable Economics and Scalable Growth

OAKLAND, Calif.–(BUSINESS WIRE)–mimik today announced the general availability of mimOE™ Studio, the first Agentix-Native Workstation, powered by mimik’s mimOE, Agentix Operating Engine. Together they enable Agentix-Native systems (aka Agentic AI) to scale agents across any hardware form factor, OS, cloud and combination of AI models. This allows developers, operators and enterprises to execute, operate and scale with certainty.

Any IDE. No rewrite. No central orchestrator. No token cost.

Today, intelligence, compute, and code generators are mature. The bottleneck is operationalization at scale: running working agents reliably across heterogeneous hardware, intermittent networks, and real-world environments. While the market chases edge AI versus cloud AI, Agentix-Native systems require both, operating seamlessly across Device-First Continuum AI and Compute. By industry estimates, 95% of AI pilots never reach production. mimOE is the answer, and mimOE Studio is the visual interface.

“In the SaaS era, businesses adapted to the services they subscribed to. Agentix-Native systems invert that. They adapt to the business. But for enterprises to trust that inversion at production scale, AI has to deliver three things SaaS never did: experimentation with controlled spend, unit economics that hold up from pilot to production, and a future-proof path to scale with full flexibility, without ripping out what’s already there,” said Fay Arjomandi, founder and CEO of mimik. “That’s what mimik delivers. We built it for ourselves first. Now every enterprise can accelerate its Agentix-Native rollout to grow the business with certainty and cost control.”

What is mimOE Studio

mimOE Studio supports the enterprise agentic AI journey from experiment to production. Developers download Studio and have a live Agentix-Native infrastructure on or across their machines in under five minutes, with no cloud account, no setup cost, and no token cost. This is a sandbox with limits on spend and risk. Studio gives a live view of every model, agent, image, trace and routing decision across the continuum, so teams see what’s running, the baseline, and what each outcome costs before scaling. Agents, models, and policies validated in Studio roll out to production on mimOE across the Device-First Continuum, with no rewrite.

What is mimOE

mimOE is a purpose-built, cross-platform Agentix-Native operating engine that enables agents to compute, network, and execute intelligently with zero-touch configuration. It runs across Linux, Windows, macOS, Android, iOS, QNX, and cloud environments, optimized for all major GPU stacks (CUDA, ROCm, Vulkan, SYCL). Built-in API and MCP gateways with three AI runtimes execute any combination of generative and predictive AI models, intelligently distributing workload between CPU and GPU, online or offline.

Installed on a device, mimOE turns it into a first-class node in an Agentix-Native infrastructure: resilient by architecture, governed by policy, and discoverable across the mesh. Built-in Zero-Trust security and Sovereignty in Execution across five dimensions let every workload run under the organization’s own authority.

By decoupling Agentix-Native system logic from the underlying heterogeneous environment, mimOE delivers its operational guarantee: Build, Execute, Operate and Scale with Certainty.

Availability and Pricing

mimOE Studio and mimOE are available today at developer.mimik.com. A free developer tier includes foundation package, documentation, and GitHub examples. Enterprise plans with onboarding, dedicated environments, and SLAs are available on request at alliances@mimik.com.

About mimik

mimik is an Agentix-Native company, a pioneer in Device-First Continuum AI and Compute. mimik’s software platform is the operating engine for enterprises and developers to scale Agentix-Native systems with certainty, with full flexibility, on their own terms. The company has partnered with major chip vendors, device OEMs, cloud providers, and system integrators, working closely with them to enable fast-track, scaled delivery of AI to organizations in manufacturing, healthcare, transportation, retail, smart buildings, and Physical AI more broadly. The outcome is business efficiency across multiple dimensions.

mimOE, mimOE Studio, Agentix Operating Engine, and Device-First Continuum AI are trademarks of mimik Technology Inc. All other trademarks are the property of their respective owners.

Contacts

mimik Contact:
PR@mimik.com

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PREAMBLE

When, in the course of the unfolding of intelligent machines, it becomes necessary for the builders of computing to dissolve the architectural assumptions which have bound the works of software to the center, and to assume among the powers of the earth the separate and equal station to which the laws of physics and the conditions of the physical world entitle them, a decent respect to the opinions of engineers, operators, and enterprises requires that they should declare the causes which impel them to the separation. We, the architects of the Agentix-Native era, hold these truths to be self-evident: that intelligence belongs where work is done; that latency is a structural cost, not a configurable parameter; that the device, the edge, and the cloud are equal members of one continuum; and that any system worthy of governing physical reality must be built, from its first principles, to live within it.

THE ARTICLES

Herein declared, ordained, and established

I. THE WORLD HAS CHANGED. THE STACK HAS NOT.

For three decades, software was built on a single, unquestioned assumption: intelligence lives in the center, and devices at the edge exist only to consume it. First it was the mainframe. Then the server rack. Then the cloud. The architectural metaphor never changed, only the size of the center. Today, as AI reshapes every industry, the same assumption is being made again: train in the cloud, infer in the cloud, orchestrate in the cloud, and send results outward to a waiting, passive world. This assumption was always a simplification. It worked because software ran on pre-determined instructions, and instructions do not need to be near reality to execute. Intelligence is different. Intelligence operates on context, and context is the reference point to reality. The closer a system sits to reality, the better its decisions. The further it sits, the more it has to collect, transmit, and reassemble data to approximate a reality it cannot see. And an agent is both client and server in construct, a structure the centralized stack was never designed to hold. In the Agentix-Native era, it is a structural liability. The physical world does not wait for a round trip. A vehicle navigating a construction zone, a surgical robot mid-procedure, a power grid responding to a frequency event, an autonomous logistics system rerouting in real time, none of these can tolerate the latency, dependency, and fragility of intelligence that lives somewhere else. The world has not only changed. It has become physical. And a software stack built for a world of passive endpoints is not equipped to govern a world of active, intelligent, physically consequential agents. The time for an architectural reckoning has arrived.

II. THE AGENTIX-NATIVE ERA DEMANDS A NEW FOUNDATION

The emergence of Agentix-Native Systems is not a feature update. It is a paradigm shift. An agent is not a smarter API call. An agent perceives its environment, reasons about what it observes, decides autonomously, acts with physical or digital consequence, and learns from the outcome. Chains of agents collaborate, delegate, negotiate, and self-organize across systems and devices forming workflows no human explicitly scripted. This is categorically different from the request-response software model that has governed computing since the 1960s. It requires a categorically different infrastructure. Existing cloud platforms were designed for stateless microservices, centralized orchestration, and deterministic pipelines. They were not designed for distributed autonomous decision-making across heterogeneous hardware, intermittent connectivity, and millisecond physical deadlines. Attempting to run Agentix-Native Systems on cloud-native infrastructure is like running a Formula 1 race on roads built for horse-drawn carriages; the physics are fundamentally wrong. What the Agentix-Native era demands is an Agentix-Native software stack and platform: one designed from first principles for agents that sense, decide, act, collaborate, and learn in the physical world, at the speed the physical world requires, with the resilience the physical world demands. mimOE is that platform.

III. THE AGENTIX OPERATING ENGINE FOR PHYSICAL AI

mimOE is not middleware. It is not a framework. It is not a thin SDK bolted onto an existing cloud architecture. mimOE is a purpose-built Agentix Operating Engine, the foundational layer where agents are operated, deployed, executed, scaled, and governed across every Physical AI form factor. Just as the Linux kernel became the universal foundation that runs from phones to data centers to satellites, mimOE is the universal runtime for inference execution and the native environment for agents. Agents operate and execute workflows intelligently across every device, edge server, and multi-cloud environment, on any existing operating system. Any CPU, GPU, or NPU. Any OS: Linux, Windows, macOS, Android, iOS, and others. Any combination of AI models: large, small, multimodal, domain-specific, predictive, and generative. Any network: broadband, 5G, satellite, Ethernet, or no network at all. mimOE abstracts across all of it, presenting a homogeneous, API-first, zero-trust operating surface that transforms any computing device into a first-class AI citizen. The laptop becomes an agent node. The vehicle becomes an agent node. The smartphone, the industrial controller, the hospital workstation, the smart camera, the robotic arm, each becomes a capable, collaborative, governed member of a living intelligence fabric. mimOE is the connective tissue of Physical AI. It is the execution engine that makes the devices of the world not just endpoints of computation, but active participants in it. This is what it means to be the de facto Agentix Operating Engine for Physical AI: not the platform that hosts intelligence, but the platform that executes it. It enables the agents to compute, collaborate, and execute intelligently across the continuum, with security and resiliency built in at scale.

IV. REAL-TIME DISCOVERY AND COLLABORATION ACROSS THE CONTINUUM

The first principle of Physical AI is that intelligence must be wherever it is needed, at the moment it is needed, without requiring a pre-configured path to get there. In the real world, devices appear and disappear. Networks fragment and reconnect. New agents are deployed and existing agents retire. Static, pre-defined routing tables and fixed orchestration topologies cannot govern a dynamic physical environment. mimOE solves this through zero-trust dynamic discovery with a capability that allows agents to find each other, authenticate each other, and begin collaborating in real time, without central coordination, across device, edge, and cloud boundaries simultaneously. When a fleet of delivery robots enters a new facility, they do not consult a central registry. They discover the facility’s local agent infrastructure, authenticate using cryptographic identity, establish trusted communication channels, and begin coordinating within seconds. When a cloud-hosted analytical agent needs real-time sensor data from a factory floor, it does not need a pre-built integration. It discovers the relevant on-device agents through mimOE’s service mesh, negotiates capability exchange, and forms a dynamic workflow on demand. This is not peer-to-peer networking layered on top of an existing stack. It is a reimagination of how distributed intelligence forms, assembles, and operates, treating the Device-First Continuum not as a network topology to manage, but as a living ecosystem of agents to choreograph. Real-time discovery and cross-environment collaboration are not features of mimOE. They are its operating model.

V. SCALABILITY AT THE SPEED OF THE PHYSICAL WORLD

The most persistent challenge in Physical AI is not building a capable agent. It is deploying ten thousand of them across heterogeneous hardware, in geographically dispersed facilities, under diverse network conditions, and updating them continuously without disrupting operations. This is the scale problem that cloud-first architectures are structurally unable to solve, because every agent at the edge that depends on centralized orchestration adds latency, cost, and a single point of failure that compounds with scale. mimOE addresses scalability through distributed intelligence management, a model in which coordination authority is pushed to the device itself rather than retained in a central control plane. The mimOE service mesh is self-organizing: as new devices and agents are enrolled, they automatically join the intelligence fabric, inherit governance policies, and begin participating in workflows without manual configuration. Model lifecycle management through mModelStore enables OTA deployment of new AI models directly to enrolled devices at fleet scale, with cryptographic integrity verification and rollback capability. The ad-hoc coordination layer enables agent-to-agent task delegation without cloud round trips, allowing complex multi-step workflows to execute entirely within the local device mesh. The result is a platform that scales horizontally with the number of devices in the world, not vertically with the size of a data center. From a single developer device to a fleet of one million industrial endpoints, mimOE provides the same architectural primitives, governance model, and operational simplicity that enable agents to manage their own distribution. Scalability in Physical AI is not a matter of adding more cloud compute. It is a matter of architecting intelligence to live where the work happens.

VI. FOLLOW THE GRAPH THAT ROUTES REALITY

Every agent in the Agentix Operating Engine for Physical AI begins with the same fundamental question: what is happening, and what does it mean for me? The answer is not found in a database query or an API call. It is found in a continuously updated, distributed knowledge graph that represents the current state of the physical world events in motion, commands issued, context shifting, conditions changing. mimOE’s Agentix-Native platform is built around this graph as the primary operational substrate. Agents follow the graph: subscribing to the event streams, command queues, and context signals that are relevant to their function, and receiving them in real time as the world changes. This is not polling. It is not batch processing. It is a living, routed intelligence fabric where significance propagates immediately from source to subscriber, regardless of whether that subscriber is on-device, on an edge server, or in the cloud. A temperature anomaly detected by a sensor agent becomes an event on the graph. A command issued by a logistics coordinator becomes a routing signal. A context shift, a vehicle entering a geofenced zone, a patient’s vitals crossing a threshold, a supply chain disruption propagating through a network becomes an intelligence update that reaches every subscribed agent instantaneously. Following the graph is how mimOE agents stay synchronized with physical reality without requiring a central authority to mediate every signal. It is how distributed intelligence remains coherent across thousands of nodes without collapsing into coordination overhead. The graph is not a data store. It is the nervous system of Physical AI.

VII. OBSERVE. EXTRACTING SIGNAL FROM A WORLD OF NOISE

The physical world does not generate clean, structured, semantically labelled data. It generates torrents of raw sensor output, unstructured events, ambiguous signals, and contextual noise. The agent that cannot distinguish what matters from what does not is not an intelligent agent; it is an expensive filter. mimOE’s Observe layer is the AI perception system of the Agentix Operating Engine for Physical AI: the capability that transforms raw physical data into signed, tagged, quality-assessed intelligence that agents can reason over, share with confidence, and act upon without ambiguity. When a mimOE agent observes its environment, it is not simply ingesting data. It is running local inference to extract semantically meaningful signals detecting anomalies, classifying situations, identifying patterns, and assigning provenance and quality metadata to every observation before it is written to the graph or shared with peer agents. Provenance matters because in a distributed multi-agent system, the trustworthiness of an observation depends on knowing which device produced it, which model processed it, under what conditions, and with what confidence. Quality tagging matters because agents making decisions that have physical consequences stopping a machine, rerouting a vehicle, escalating a medical alert must know not just what was observed, but how much to trust it. This signed, attributed intelligence model is what separates mimOE’s Observe capability from simple edge inference. It creates an auditable, trustworthy intelligence chain from physical sensor to agent decision the foundation on which Physical AI governance and accountability are built.

VIII. RESPOND. ACTING WHERE IT MATTERS, WHEN IT MATTERS

Observation without response is not intelligence. The value of Physical AI is realized in the moment an agent acts: locally, collaboratively, appropriately, and gracefully regardless of what the network is doing. mimOE’s Respond layer encodes this principle as a first-class architectural commitment, not a configurable option. When an agent’s observations warrant a response, mimOE enables agents to choose from four response modes that mirror the full spectrum of physical scenarios. Acting locally on device means that the most time-critical responses a safety system intervention, a real-time control adjustment, an immediate alert execute in microseconds on the device that detected the trigger, without any network dependency whatsoever. Coordinating with or dispatching to another agent means that responses requiring capabilities or context beyond the local device are handled through direct agent-to-agent collaboration within the mimOE mesh, preserving low latency while extending the response envelope. Escalating to a human when required means that mimOE agents know their own limits: when confidence is insufficient, when stakes exceed autonomous authority, or when regulatory compliance demands human oversight, the platform routes to human decision-makers with full context attached. Degrading gracefully when offline means that connectivity loss is not a failure mode it is a routine operating condition that mimOE handles by continuing to execute autonomously with the intelligence available locally, queuing results for synchronization, and maintaining full observability of the degraded period. Together, these four response modes define what it means for an agent to be truly resilient in a physical environment. Response is not a feature of Physical AI. It is its purpose.

IX. LEARN. THE INTELLIGENCE THAT COMPOUNDS

An agent that cannot learn is a system. An agent that learns is an asset. The distinction matters enormously in Physical AI, where the environments, conditions, and requirements that agents operate in change continuously and where the gap between a model trained in a laboratory and a model shaped by real operational experience is the gap between a proof of concept and a deployed product. mimOE’s Learn layer closes this gap by treating every agent interaction, every outcome, and every context shift as a training signal not in a future retraining cycle, but continuously, in the Agentix Operating Engine itself. When a mimOE agent completes a task, it writes signed results back to the graph: not just the output, but the reasoning path, the confidence levels, the environmental conditions, and the observed outcome. These signed result records become the raw material for three compounding forms of learning: model and policy updates that incorporate operational experience to improve future decision quality; pattern codification that identifies repeatable situations and encodes proven response strategies for immediate reuse across the agent fleet; and federated learning that aggregates insights from thousands of devices without centralizing sensitive raw data, enabling the entire fleet to benefit from every individual agent’s experience. This is how mimOE turns the scale of Physical AI deployment into a competitive advantage: every device enrolled, every agent operating, every situation observed and responded to makes the entire intelligence fabric smarter. The fleet learns as one. Models improve in the field. Patterns discovered at one site propagate to every site. What begins as deployed intelligence becomes accumulating intelligence. And accumulating intelligence is the only durable advantage in a world where models can be copied but operational wisdom cannot.

X. THE MOVEMENT BEGINS NOW

The inflection point for Physical AI is not coming. It is here. Every paradigm shift in computing has needed a moment where a foundational infrastructure layer became universally available, gave developers a common vocabulary, and assembled an ecosystem that turned individual experiments into a global movement. mimOE offers the same thing to the builders, operators, and enterprises of the Physical AI era. Not a replacement for the models, the hardware, or the cloud investments already made, but the Agentix Operating Engine that makes them deployable in the physical world, at the scale the physical world requires. The mimOE Developer Program is open. The tools are available. The chip vendor integrations are live. The OEM partnerships are being formed. And the ecosystem of developers writing their first Physical AI agents, of enterprises deploying their first intelligent device fleets, of system integrators building the vertical solutions that will define their industries for the next decade is assembling now. Every technology movement in history has been defined by the moment a critical infrastructure layer became universally available and universally understood. The TCP/IP stack for the internet. The Linux kernel for open computing. The container runtime for cloud-native software. mimOE is that layer for Physical AI: the Agentix-Native foundation that every device in the world can run, every agent in the world can trust, and every enterprise in the world can build on from the Device-First Continuum to the cloud and back again. The physical world is awakening to intelligence. mimOE is how it learns to think.

❖    ⁂    ❖    ⁂    ❖

IN WITNESS WHEREOF

we, the architects, engineers, and stewards of mimik Technology, do hereby publish and declare that this Manifesto sets forth the foundational principles of the Agentix Operating Engine for Physical AI, and that mimOE shall stand as the universal substrate upon which the intelligence of devices, edges, and clouds is operated, executed, and made trustworthy across the continuum.

The post THE PHYSICAL AI MANIFESTO first appeared on mimik.

As on-device AI agents proliferate across domains such as health monitoring, industrial IoT, smart infrastructure, and personal assistants, developers face a key architectural choice: build agents within application-native service frameworks or deploy them as independently managed microservice agents in a dedicated operating environment. We term these paradigms app-native and Agentix-native. In the app-native approach, agents are implemented within a single application using platform IPC mechanisms (for example, Android Bound Services, iOS XPC, and Linux D-Bus), which effectively produce monolithic systems with tightly coupled deployment and failure domains. In contrast, the Agentix-native model packages each agent as a micro intelligence module (mim), a serverless microservice running on a lightweight operating environment (mimOE) that provides lifecycle management, service discovery, and coordination across the device–cloud continuum.

This paper presents a systematic engineering comparison of the two paradigms within the Device-First Continuum AI (DFC-AI) framework. Using analytical models derived from published benchmarks, we evaluate trade-offs across dimensions including evolvability, composability, inter-agent reasoning, regulatory modularity, continuum mobility, operational overhead, and system resilience. Our analysis highlights the architectural implications of serverless microservice agents, including dynamic composition, independent lifecycles, and shared model registries with caching and deduplication. While monolithic app-native systems may offer modest raw efficiency advantages, the Agentix-native architecture provides stronger modularity, resilience, and deployment flexibility. More fundamentally, because real-world agents must communicate beyond a single application process, a dedicated agent operating environment emerges not simply as an optimization but as a prerequisite for scalable multi-agent systems.

The post App-Native vs. Agentix-Native Architectures for On-Device AI Agents first appeared on mimik.

Software-defined vehicles are no longer theoretical. They are already on the road; running increasingly complex software stacks that manage safety systems, autonomy features, diagnostics, infotainment and user experiences. However, as vehicles evolve there advanced features depend heavily continuous cloud connectivity. mimik solves fundamental constraint by delivering in-vehicle intelligence that can run locally in the vehicle, while remaining seamlessly connected to a central cloud if needed. Its SOAFee Blueprint Architecture runs intelligent, offline-capable AI agents in-vehicle without restructuring the existing hardware stack.

From Cloud-First to Vehicle-First Intelligence

mimik’s platform underpins software-defined-vehicle (SDV) features such as hyper-personalization, real-time decision making, predictive maintenance, usage-based insurance and context-aware infotainment by processing data at the edge or in the vehicle. mimik’s approach aligns with SOAFEE’s vision by shifting intelligence into the vehicle itself, while maintaining interoperability with cloud services when available. The result is a hybrid execution model where services, APIs, and AI workloads operate locally by default and extend outward only when connectivity allows.

This architectural shift enables vehicles to function as autonomous, resilient computing environments, rather than thin clients tethered to the cloud.

mim OE: The Agentix-Native Runtime Inside the Vehicle

At the core of mimik’s SOAFEE Blueprint implementation is mim OE, mimik’s Agentix-Native execution layer. mim OE gives automakers a continuous cloud-native runtime across vehicle computing devices, letting them develop, deploy and manage workloads like microservices and AI agents directly inside the vehicle. AI-native access to vehicle functions in the SOAFEE blueprint, are exposed as callable tools for AI agents, letting agentic apps compose and adapt vehicle behaviours dynamically at the edge.

Unlike traditional runtimes, mim OE is designed for distributed intelligence:

• Services and AI agents run locally, not remotely
• Nodes discover each other dynamically
• Interactions continue safely even when disconnected

Making SOAFEE Operational—Without Re-Architecture

SOAFEE defines how automotive software should be structured and deployed. mim OE makes that structure operational in real-world vehicles.

With mim OE, SOAFEE-aligned workloads can be introduced in two complementary ways:

1. Native microservices implementing new functionality
2. Proxy agents that expose existing legacy systems as APIs

This dual model removes the need for a “rip-and-replace” strategy. Legacy investments remain intact while new capabilities are layered on top, allowing OEMs to modernize at their own pace.

Extending SOAFEE into the AI Domain with MCP

As vehicles incorporate more AI-driven behavior, exposing services alone is no longer sufficient. AI systems need a structured way to discover, negotiate, and consume vehicle capabilities. mimik addresses this by integrating the Model Context Protocol (MCP) into its SOAFEE Blueprint implementation. SOAFEE standardizes what vehicle services are available and MCP defines how AI agents interact with those services.

Blueprint in Action: A Practical Example

In a SOAFEE-aligned vehicle running mixed operating systems (QNX, Linux, Android), mim OE enables:

• In-vehicle services (e.g., comfort controls, access management) to run as microservices
• Secure key management via agent-based access control
• Driver smartphones to act as temporary service aggregators
• Seamless bridging between automotive and non-automotive environments

The vehicle effectively becomes a super-gateway, connecting embedded automotive systems with AI-enabled consumer devices—without sacrificing safety or reliability.

Why This Matters for OEMs and Tier 1s

mimik’s SOAFEE Blueprint implementation delivers tangible advantages:

• Standards alignment without vendor lock-in
• Offline-first operation for safety-critical systems
• Incremental adoption that protects existing investments
• AI readiness through MCP-enabled agent interaction
• Future scalability across evolving hardware and software stacks

Rather than choosing between compliance and innovation, OEMs gain both.

Watch the Technical Walkthrough

To see the SOAFEE Blueprint with mim OE and MCP in action, watch the full technical walkthrough below.

Moving Forward with SOAFEE and mimik

mimik continues to collaborate with the SOAFEE community to advance practical, deployable architectures for software-defined vehicles.

Opportunities include:

• Proof-of-concept programs
• Integration pilots
• Joint Blueprint evolution

Whether you are building next-generation vehicle platforms or extending existing ones, mimik provides a path to resilient, AI-native, in-vehicle intelligence—aligned with SOAFEE standards and designed for real-world conditions.

The post mimik Brings In-Vehicle API, AI Inference and MCP Gateway to SOAFEE Blueprint Architecture first appeared on mimik.

Modern multi-agent systems ranging from sensor networks monitoring critical infrastructure to crowdsourcing platforms aggregating human intelligence can suffer significant performance degradation due to systematic biases that vary with environmental conditions. Current approaches either ignore these biases, leading to suboptimal decisions, or require expensive calibration procedures that are often infeasible in practice. This performance gap has real consequences: inaccurate environmental monitoring, unreliable financial predictions, and flawed aggregation of human judgments. This paper addresses the fundamental question: when can we learn and correct for these unknown biases to recover near-optimal performance, and when is such learning futile? We develop a theoretical framework that decomposes biases into learnable systematic components and irreducible stochastic components, introducing the concept of learnability ratio as the fraction of bias variance predictable from observable covariates. This ratio determines whether bias learning is worthwhile for a given system. We prove that the achievable performance improvement is fundamentally bounded by this learnability ratio, providing system designers with quantitative guidance on when to invest in bias learning versus simpler approaches. We present the Adaptive Bias Learning and Optimal Combining (ABLOC) algorithm, which iteratively learns bias-correcting transformations while optimizing combination weights through closedform solutions, guaranteeing convergence to these theoretical bounds. Experimental validation demonstrates that systems with high learnability ratios can recover significant performance (we achieved 40%-70% of theoretical maximum improvement in our examples), while those with low learnability show minimal benefit, validating our diagnostic criteria for practical deployment decisions.

Published on Arxiv

The post Optimal Information Combining for Multi-Agent Systems Using Adaptive Bias Learning first appeared on mimik.

This study introduces Device First Continuum AI (DFC-AI), a transformative architecture within the Hybrid Edge Cloud paradigm designed to address the limitations of traditional cloud-centric artificial intelligence across diverse applications. DFC-AI prioritizes the deployment of intelligent agents, built on a microservices framework, that originates and primarily resides on end devices, extending to gateways and cloud servers as needed. This Device-First approach is essential for enabling real-time decision-making and personalized experiences for both industrial and consumer applications, particularly in scenarios demanding low latency, operation in disconnected environments, and efficient management of massive data streams. The study highlights the fundamental challenges of relying solely on centralized cloud or basic edge computing models, including prohibitive bandwidth costs, energy inefficiency, and compromised user privacy. By embedding intelligence at the device level, DFC-AI overcomes these limitations, fostering autonomous operation, seamless collaboration among devices, and substantial reductions in operational overhead, moving us closer to realizing the potential of truly human-like artificial intelligence in machines. Through illustrative examples spanning various sectors, this study demonstrates the potential of DFC-AI to unlock a new era of holistic, responsive, and user-centric intelligent systems, paving the way for innovative applications and enhanced digital experiences in an increasingly connected world.

The post Device First Continuum AI (DFC-AI): Realizing Human-Like AI first appeared on mimik.

Industrial automation in the energy sector requires AI systems that can operate autonomously regardless of network availability, a requirement that cloud-centric architectures cannot meet. This paper evaluates the application of Device-First Continuum AI (DFC-AI) to critical energy sector operations. DFC-AI, a specialized architecture within the Hybrid Edge Cloud paradigm, implements intelligent agents using a microservices architecture that originates at end devices and extends across the computational continuum. Through comprehensive simulations of energy sector scenarios including drone inspections, sensor networks, and worker safety systems, we demonstrate that DFC-AI maintains full operational capability during network outages while cloud and gateway-based systems experience complete or partial failure. Our analysis reveals that zero-configuration GPU discovery and heterogeneous device clustering are particularly well-suited for energy sector deployments, where specialized nodes can handle intensive AI workloads for entire fleets of inspection drones or sensor networks. The evaluation shows that DFC-AI achieves significant latency reduction and energy savings compared to cloud architectures. Additionally, we find that gateway based edge solutions can paradoxically cost more than cloud solutions for certain energy sector workloads due to infrastructure overhead, while DFC-AI can consistently provide cost savings by leveraging enterprise-owned devices. These findings, validated through rigorous statistical analysis, establish that DFC-AI addresses the unique challenges of energy sector operations, ensuring intelligent agents remain available and functional in remote oil fields, offshore platforms, and other challenging environments characteristic of the industry.

Published on Arxiv

The post Evaluating Device-First Continuum AI (DFC-AI) for Autonomous Operations in the Energy Sector first appeared on mimik.

Make AWS Spot Instances Operationally Impactful for AI and Mission-Critical Workloads with mimik

Most enterprises love the 90% savings AWS Spot Instances deliver but hesitate to trust Spot with anything operations or revenue critical. mimik changes that equation by turning Spot into a faster, more flexible and more intelligent compute fabric that spans your AWS estate and your endpoints.

The problem: Spot is inexpensive, but not operationally impactful or business-friendly

Spot is brilliant for cost savings, but it was never designed around your most important workloads. You get a two‑minute interruption notice; then it is your responsibility to scramble options. Either pre-warm capacity (resources) with the runtime stack – Docker, Kubernetes and VMware – otherwise it takes over 12 mins to start the stack – to maintain operational continuity so you can dynamically move your workload or restart the workload on another resource and begin the operation from scratch. The former is costly and inefficient, while the latter is cumbersome and unpredictable.  The result:

• Teams limit Spot to expendable batch jobs.
• Real-time and customer-facing services stay on more expensive, overprovisioned on‑demand capacity.

In an AI-first world where workloads are more dynamic, contextual, and latency-sensitive, this model simply does not scale.

Enter mim OE: a lightweight Spot fabric

mimik’s operating and execution environment, mim OE, is a lightweight microservice runtime and control plane that runs both on cloud, AWS (Spot and on‑demand), on-prem, gateways and on endpoint devices such as drones, cameras, smartphones, robots, and industrial PCs. Because the mim OE runtime is a serverless environment – negligible pre-warm overhead and sub‑second startup – you no longer need to keep “hot” VMs alive just in case a Spot interruption happens. Instead of managing VMs, you describe microservices and agents once, let mim OE dynamically move the service to another resource within even less than 2 minutes’ notice time and can also decide where they should run at any given moment, whether on a Spot instance, an on‑demand node, or an endpoint.

From “best-effort batch” to “near-continuous” Spot

Today, initializing the runtime stack can take 15 minutes or more, which is far beyond the two-minute Spot termination window. mim OE and its utility agents collapse that gap by:

• Keeping microservices pre-warmed in a lightweight runtime
• Automatically moving them between available worker resources when Spot capacity shifts
• Maintaining stable service addresses so callers never need to know which node is active

For your team, this feels like Spot with continuity. Workloads can dynamically move without operational interruptions, making Spot viable for real production services that are stateless and state-light – not just back-office batch jobs.

Extending Spot beyond the data center

The biggest shift mimik unlocks is conceptual: Spot no longer needs to stop at the boundary of your AWS region. With mim OE, compute no longer ends at your cloud boundary. Your endpoint devices become first-class participants in the same elastic pool as EC2 capacity, allowing compute-enabled devices such as gateways, PCs, mobile phones, drones, robots, etc. to execute microservices and agents locally for low-latency inference execution and filtering. When a task exceeds local, proximity and account-level compute capacity, mim OE can escalate it to EC2 Spot, NVIDIA GPUs, or private clouds automatically.

This “Spot everywhere” model reduces bandwidth costs, delivers energy efficiencies, sharpens user experience, and lets you treat AWS as the broker of a global device-first continuum of compute rather than just traditional centralized or on-prem cloud.

Direct business value for AWS enterprises

For enterprise leaders, this is not just an architecture story. It is a P&L story. Faster restart and smarter placement make it safer to move more workloads from on‑demand to Spot, without designing everything as expendable. You also reduce idle and energy costs by eliminating large pools of pre-warmed containers and VMs because mim OE’s lightweight runtime consumes very little energy even when services are standing by.

This creates new revenue and margin opportunities. Idle capacity on endpoints can be monetized through an AWS-managed marketplace, while AWS (or you, in a platform role) can capture brokerage value when workloads escalate to GPU clouds or other hyperscalers. mimik enables agentix-native AI workloads to run as parallel, context-aware microservices that collaborate across endpoints and Spot instances, rather than being trapped inside monolithic VM deployment cycles.

For many enterprises, this is the missing layer between today’s cloud architectures and the “continuum intelligence” vision enterprises desire to deliver.

What it takes to get there

There are practical considerations before you turn Spot into a global fabric:

• Architectural complexity: mim OE introduces a powerful but new control plane with a rich suite of utility agents for discovery, workload placement, and service coordination that must operate alongside existing AWS tools and Spot Fleets.
• Application refactoring: To fully benefit, workloads need to move towards microservice/agent patterns, externalizing state and tolerating mobility between devices and EC2(s).
• Security and governance: Running Spot-style workloads across cloud and endpoints demands strong identity, token management, encryption, and data placement controls. However, do not fret as mimik has all the tools to make these steps simple.

The upside is a Spot strategy that aligns with where your business is actually going, real-time, AI-driven, distributed, and margin-conscious.

Call to action

If your AWS team is under pressure to cut cloud spend without sacrificing SLAs, make AI workloads more responsive, or turn the devices you already own into part of your compute advantage, then it is time to look at how mimik and AWS Spot can work together. mim OE does not replace AWS Spot. It upgrades it, transforming “cheap spare capacity” into a distributed continuum of intelligence that spans your data centers, your edge, and everything in between. If you are interested in piloting this model in your AWS environment or simply want to explore what a Spot-powered continuum could look like for your architecture, reach out to the mimik team and start the conversation.

The post Make AWS Spot Instances Operationally Impactful for AI and Mission-Critical Workloads with mimik first appeared on mimik.

Today, many organizations struggle to roll out AI due to high infrastructure costs, compatibility issues, security concerns, and system fragmentation. mimik and Advantech aim to solve these challenges by combining their strengths: Advantech’s comprehensive range of Edge AI hardware and mimik’s agentix-native software systems for edge-based AI workflow automation.

At the heart of this collaboration is mimik’s “agentix-native” platform, which allows devices to automatically discover each other and work together without manual setup. Once connected, devices instantly become operational – no matter the hardware, operating system, or AI model in use. This allows businesses to run AI applications more flexibly, securely, and cost-effectively, without needing to rebuild their systems every time a new device or AI model is added.

“This partnership brings together the best of both worlds – mimik’s agentic AI software and Advantech’s comprehensive Edge AI platform,” said Linda Tsai, President of the Intelligent System Sector at Advantech. “It empowers our customers to roll out smart, secure, and collaborative Edge AI systems in sectors like manufacturing, transportation, healthcare, and defense – without limitations.”

“Dynamic discoverability with built-in zero-trust security is not a feature, it’s the strategic foundation for collaborative autonomy,” said Fay Arjomandi, Founder and CEO of mimik. “This partnership transforms what might otherwise appear as a fragmented array of hardware, ranging from cameras and drones to industrial gateways, rugged PCs, and hyperscaler systems, into a unified, adaptive compute continuum. With mimik’s dynamic discovery and runtime software layered across this spectrum, enterprises can choreograph agentic workloads on the fly, without being locked into any single model or compute provider. It’s not just a more flexible AI deployment model; it’s a smarter business model. That’s the promise of mimik: YOUR ROI FOR AI.”

About mimik

mimik powers the Agentic Economy with Agentix-Native software that turns everyday devices into intelligent collaborative systems. Its software platform enables real-time inference across smartphones, cameras, drones, robots, machines, and servers. By creating a Device-First AI continuum across endpoint devices and the cloud, mimik gives way to enterprises to operationalize agentic AI, scale intelligence, and optimize performance and cost. For more info visit https://mimik.com/

mimik Contact:

PR@mimik.com

About Advantech:

Advantech’s corporate vision is to enable an intelligent planet. The company is a global leader in the fields of IoT intelligent systems and embedded platforms. To embrace the trends of IoT, big data, and artificial intelligence, Advantech promotes IoT hardware and software solutions with the Edge Intelligence WISE-PaaS core to assist business partners and clients in connecting their industrial chains. Advantech is also working with business partners to co-create business ecosystems that accelerate the goal of industrial intelligence. (www.advantech.com)

Advantech Contact:

Alyse.Ho@advantech.com.tw

Tel: +886-2-2792-7818, Ext. 1236

https://www.businesswire.com/news/home/20250708504992/en/Advantech-and-mimik-Join-Forces-to-Simplify-AI-Deployment-Across-Edge-and-Cloud?utm_campaign=shareaholic&utm_medium=copy_link&utm_source=bookmark

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The center is a Physical AI production-first environment that trains, certifies, and supports developers and enterprises in building agentic-native workflows. These workflows mimic real-world business operations and execute autonomously across devices. Empowered with the right tools, developers and enterprises will be able to easily operationalize Agentic Native AI solutions.

The partnership will combine mimik’s Device-First Continuum AI Execution Fabric with Tech Mahindra’s deep engineering expertise in end compute systems and devices stack across industries such as automotive, communications, industrial, etc. This combination will enable quick and seamless introduction and integration of new features for OEMs, leveraging AI agents that operate with real-time, context-aware intelligence across a wide range of end compute stacks, including SDVs, smartphones, drones, robots, and industrial sensors. These agents are designed to function offline first, without constant connectivity to the cloud, while remaining capable of using any cloud service when needed.

“This is where physical AI becomes real,” said Fay Arjomandi, Founder and CEO of mimik. “We’re helping enterprises move beyond prototypes and dashboards to deploy AI agents that work autonomously on the entire continuum compute fabric, including across everyday devices like smartphones, drones, and robots, mirroring real-world processes and to any cloud as needed, unlocking real economic value.”

Narasimham RV, President – Engineering Services, Tech Mahindra, said, “As organizations push product and operational boundaries, the need for real-world, autonomous AI systems is more critical than ever. Our partnership with mimik to launch the Agentic AI Production Center is a significant step towards enabling hypercognition and rapid innovation at the edge, maximizing the potential of physical-digital interplay. This opens pivotal opportunities for organizations to evolve products faster and stay ahead in their transformation journey.”

The partnership reflects Tech Mahindra’s promise of Scale at Speed™ with a platform-led, AI-driven approach to product engineering. The Agentic Economy is here, providing reach and acceleration into areas that weren’t possible earlier, auguring a new movement in product innovation.

About mimik

mimik powers the Agentic Economy with Agentix-Native software that turns everyday devices into intelligent collaborative systems. Its software platform enables real-time inference across smartphones, cameras, drones, robots, machines, and servers. By creating a Device-First AI continuum across endpoint devices and the cloud, mimik gives way to enterprises to operationalize agentic AI, scale intelligence, and optimize performance and cost. For more information on how mimik can Partner with you, please visit: https://mimik.com

About Tech Mahindra

Tech Mahindra (NSE: TECHM) offers technology consulting and digital solutions to global enterprises across industries, enabling transformative scale at unparalleled speed. With 150,000+ professionals across 90+ countries helping 1100+ clients, Tech Mahindra provides a full spectrum of services including consulting, information technology, enterprise applications, business process services, engineering services, network services, customer experience & design, AI & analytics, and cloud & infrastructure services. It is the first Indian company in the world to have been awarded the Sustainable Markets Initiative’s Terra Carta Seal, which recognizes global companies that are actively leading the charge to create a climate and nature-positive future. Tech Mahindra is part of the Mahindra Group, founded in 1945, one of the largest and most admired multinational federation of companies. For more information on how TechM can partner with you to meet your Scale at Speed™ imperatives, please visit https://www.techmahindra.com

Contacts

Press Contact:
PR@mimik.com

Press Contact:
media.relations@techmahindra.com

https://www.businesswire.com/news/home/20250630917270/en/mimik-and-Tech-Mahindra-Unveil-a-Pioneering-Agentic-AI-Production-Center

The post mimik and Tech Mahindra Unveil a Pioneering Agentic AI Production Center first appeared on mimik.