Why the existing IT and DevOps stack does not reach the agent runtime, where the gap lives in your architecture, and what closes it without ripping out anything you built.

An open-source AI agent crossed 134,000 GitHub stars before most procurement teams had heard its name. It is installed on employee laptops today. It reads their email, drives their browser, executes shell commands, and runs continuously in the background. Your firewall does not see it. Your VPN does not protect against it. Your identity stack does not authenticate it. Your SIEM cannot describe what it does.

This is not a hypothetical from a Gartner deck. This is your current attack surface.

And it does not stop at the laptop. The same gap runs through every device the enterprise operates: phones, controllers, robots, vehicles, gateways, servers. The laptop is the first example because the laptop is the most visible. The architectural problem is the same on every endpoint.

The IT and DevOps stack inside every enterprise was built over a decade of patient investment. It enrolls devices, authenticates users, authorizes access, deploys applications, monitors traffic, and audits compliance. It is intact and it is necessary. It is also architecturally incomplete for the era that just arrived. The agents your employees are running today operate outside every control point your stack was designed to enforce, and they take real actions, against real systems, on real corporate data.

This post is for the leaders accountable for that gap: the CIO, the CISO, the CTO, Platform Engineering, DevOps, SRE, and the developers themselves. It names the stack you built, names what just arrived on top of it, names why current controls cannot reach the new layer, and shows where the gap closes. The path forward is not rip and replace. It is extend. And the extension has a specific structural shape.

The stack you built

Walk into the average enterprise IT and DevOps function today and you find a layered system that took ten years and a generation of vendor consolidation to assemble. Each layer governs a specific scope. Each layer has a specific reach.

At the network and identity edge: a perimeter you no longer trust (firewalls, VPN, ZTNA), an identity and access management plane that authenticates and authorizes users (Okta, Microsoft Entra, Ping), and SaaS access control (Netskope, Zscaler, CASB). At the endpoint and device: a fleet you manage by policy (Microsoft Intune, Jamf, Workspace ONE, Kandji), an endpoint protection platform that watches the binaries (CrowdStrike, SentinelOne, Microsoft Defender), and an asset inventory that knows what’s deployed where. At the workload and data: SIEM and SOAR that ingest the telemetry (Splunk, Sentinel, Chronicle), DLP that classifies and gates data movement, and observability platforms that watch the services (Datadog, Splunk, New Relic). In the engineering pipeline: source control, CI/CD, secrets management, artifact registries, change management, and incident response.

This stack covers four things, mostly well: users (who they are, what they can access), devices (whether they are managed, encrypted, and compliant), networks (what flows where), and applications (deployed by your DevOps pipelines into your environments).

It does not cover the actor that just arrived: the autonomous software agent that runs on the employee’s machine, in the employee’s session, against your systems and your data.

What just arrived

The shift is already in production. Not in a pilot, not in next year’s plan, but in the wild. Three categories of software are the new actors.

The first is the autonomous coding agent. Claude Code, OpenClaw, Cline, Aider, and a growing set of open-source variants run in the developer’s terminal or editor. They write code, execute it, fix the errors, commit, push, and iterate. They are continuous: not a single chat session but a long-lived process with persistent state. They have shell access. They have file system access. They use the developer’s credentials. They make commits in the developer’s name.

The second is the computer-use agent. Software that takes screenshots of the user’s desktop, decides where to click, types into form fields, opens browsers, sends email, reads documents. It does what the user does, only it does it autonomously, often unattended, and at machine speed.

The third is the in-application agent assistant. The “ask AI” button inside Notion, the Copilot inside the IDE, the AI agent in the help desk tool, the agent embedded in the data analytics platform. Each one is small. Together they multiply. Each one runs with permissions the user granted it, often once, often broadly.

None of these were procured. None were deployed by IT. None show up in your CMDB. None of them are governed by the policies your stack enforces because the stack does not see them as actors. It sees, at best, a process running under a user, on a managed device, making outbound HTTPS calls. The process is approved. The device is compliant. The traffic is encrypted. To the stack, everything looks normal. The actor inside is invisible.

The laptop is the most visible example because it sits in the developer’s hands and the SOC’s nightmares. The same actor class is appearing across the enterprise endpoint estate. Mobile devices run AI assistants that read messages, schedule meetings, and execute on behalf of the user. Industrial controllers in manufacturing plants run inference at the edge and increasingly act autonomously on the production line. Robots in warehouses, smart cameras in retail, in-vehicle compute in fleet operations, and embedded systems in smart buildings all run agents that take real actions against enterprise systems, against enterprise data, with the same governance gap. The IT and DevOps team’s responsibility does not end at the laptop. The stack has to reach every device.

Agents enter like people, not like applications

Here is the structural shift the existing stack was not designed for. Software arrived for the past three decades as applications. Applications are containers. You deploy them. You patch them. You inventory them. You monitor their processes and their network. You apply policy at the network and identity boundaries around them. The application itself is largely passive until invoked, and when invoked, what it does is bounded by what its code can do.

An agent is not an application. An agent is an actor that arrived inside the building like a person.

When a new employee starts, you do not just check whether their laptop is compliant. You issue them an identity. You grant them authorization scoped to their role. You define what they can access in which systems. You log what they do. You can revoke their access. They have an HR record, a manager, a department, a project assignment. They have a floor pass that tells the building what doors open for them and which do not. They have a digital floor pass that tells the systems what data they can see and what actions they can take.

An agent now needs all of that, and the stack you built does not issue any of it for them.

An agent is a software actor that needs an identity distinct from the user, authority scoped to a task, observability tied to its identity, and a lifecycle that ends when its purpose ends. None of these are properties of an installed application. All of these are properties of a person.

The IT and DevOps stack already knows how to do these things. It does them for people. It does them for service accounts. It does not do them for autonomous agents running inside the user’s session.

Why current controls cannot reach

Each layer of the existing stack has a specific reach. None of them reach where the agent executes.

Network and perimeter governs flows in and out. The agent runs inside the trusted boundary. Its traffic looks like the user’s traffic. The agent is a process on a known machine; the perimeter cannot tell it is not the user.

Identity and IAM governs who the user is and what the user can access. The agent borrows the user’s identity. There is no distinct agent principal in your IDP. There is no scoped credential for the agent. There is no way to revoke the agent without revoking the user.

Endpoint protection (EDR) governs binaries on the device. The agent is an approved binary. The signatures are clean. The runtime telemetry shows a process under a user. EDR confirms the binary is fine and reports the device as healthy. The actions the agent takes against your systems are not in scope.

SaaS access and CASB governs the SaaS connections users make. The agent makes those connections under the user’s authentication. The CASB sees the SaaS traffic but cannot distinguish the agent from the user.

SIEM and observability govern ingest telemetry that already lacks an agent identity. There is no event class for “agent X took action Y in context Z.” The SIEM gets a process trace, not a semantic record.

CI/CD and DevOps pipelines govern code you wrote and deployed. The agent is not your code. You did not deploy it through your pipelines. There is no dev/staging/prod for the agent. There is no rollback.

Every layer is doing exactly what it was designed to do. Every layer is intact. The actor is the wrong unit of management. The stack was designed for users, devices, applications, and code. The agent is none of those.

Device management reaches the device. It does not reach the agent.

One layer of the stack already reaches the laptop, the desktop, and the phone. It is the device and remote management layer, and any IT leader will reasonably ask whether it already covers this. It does not, and the reason is precise.

Device and remote management is the one control plane built to operate on the endpoint itself. MDM and UEM platforms such as Microsoft Intune, Jamf, Omnissa Workspace ONE, and Kandji enroll the device, enforce its operating system configuration and compliance, deploy and inventory the approved applications, and tie all of it to the user and the device posture. Where the rest of the stack stops at the network or the cloud, this is the layer that actually lands on the machine.

But its unit of management is the device and the user, and its model of software is the installed application. It can push an agent onto the laptop as a managed app, confirm the device is encrypted and compliant, and report the fleet as healthy. What it cannot do is treat the agent as an actor. There is no agent identity distinct from the user. There is no authority scoped to a single agent and a single task. There is no semantic record of what the agent did, against what, and on whose behalf. There is no quota, no rate limit, and no kill switch at the granularity of the agent.

To the management console, a compliant laptop running an approved binary is a solved problem, even while that binary autonomously takes thousands of actions under the employee’s identity.

The channel this layer holds to the device, waking it to sync, pulling compliance state, and pushing configuration, is a channel to the operating system and its managed apps. The agent runs above that channel, inside the user’s session, and stays invisible to it. This is the same gap that runs through the rest of the stack, now appearing in the layer closest to the metal. Device management governs the container the agent runs in. It does not govern the agent.

The structural answer is not to replace device management. It is necessary, and it stays. The answer is a new management layer that sits on top of it, takes the managed and compliant device as its trusted foundation, and governs the agents running on that device as first-class actors.

The new questions IT and DevOps have to answer

If the existing stack does not reach the runtime where agents execute, then the questions are now structurally different. Eight of them, drawn from the operational disciplines IT and DevOps already apply to everything else they manage.

These are not aspirational. These are the same disciplines you already apply to users (identity, role, audit, offboarding), to services (deployment, observability, rate limiting, kill switch), and to data (classification, access policy, lineage). The agent is a new unit of management, and the operational pattern is the one you already know. It just has to be applied where the agent actually executes.

There is a ninth question behind the eight. Every question above treats the agent as a single actor working against enterprise systems. That is where governance starts, and it is not where agentic systems stop. The moment two agents collaborate, on the same device or across the device estate, the governance problem becomes an interaction problem: who directs the work, how peers maintain a shared picture of identity and trust, how an event on one device triggers action on another. Our CTO, Michel Burger, wrote about the three modes of agent interaction, orchestration, coordination, and choreography, and why an Agentix-Native system runs on all three at every layer. The two questions compose. Governance defines what one agent may do. The interaction modes define how many agents act together. The runtime has to enforce policy in all three modes, not just the first.

Where mimOE sits, and why there

The shape of the answer is now visible. There is a new layer of the IT and DevOps stack. It sits at the runtime where the agent executes. It treats the agent as a first-class actor. It exposes agent identity upward into IAM, agent telemetry upward into SIEM, agent lifecycle upward into ITSM. It consumes the managed device as its foundation. It does not duplicate any layer beneath it.

mimik built that layer. It is called mimOE, the Agentix Operating Engine. The architectural placement is deliberate, and the reasoning matters more than the assertion.

Why the runtime, not the perimeter

The agent’s actions originate at the runtime, on the device. Everything downstream of that is consequence. If governance enters only after the agent’s request leaves the device, the policy decision is being made by a layer that no longer has full context. The user’s identity is on the wire but the agent’s identity is not. The action’s intent is implicit but never declared. The policy enforcement point ends up trying to reconstruct what should have been governed at the source. Perimeter retrofits cannot reach this. They are not where the agent decides.

Why a new layer, not a feature of an existing one

The agent layer is structurally different from every other layer of the stack, and that difference makes it impossible to fold into any of them. IAM governs users and service accounts and the agent is neither. EDR governs binaries on the device and the agent’s behavior is not a property of its binary. CASB governs SaaS access and the agent operates inside user sessions across many SaaS and non-SaaS targets. MDM governs the device as a managed unit and the agent runs above the OS. The agent is its own unit of management. Like every prior unit of management in the history of enterprise IT (user, device, application, service), it needs its own layer.

Why on top of the device, not in the cloud

The cloud-resident option exists. There are agent runtimes that operate from a control plane in the cloud and reach down to the device through APIs or proxies. The structural problem with that placement is the same one the rest of the stack has. By the time the agent’s intent reaches the cloud, the moment of action has already passed. The cloud runtime can govern the next request but not the current one. It cannot enforce policy at the moment of action because the moment has elapsed by the time the cloud sees it. And the cloud runtime cannot operate when the device is offline, partially connected, or operating in a sovereign network the cloud does not enter. The runtime layer for agents has to live where the agents do, which is on the device.

The continuum claim

The same operating engine extends beyond the laptop. mimOE runs on every class of enterprise endpoint: laptops and desktops where developers and knowledge workers run their agents, mobile devices where in-application agents operate, industrial controllers and embedded systems where agentic inference drives production, robots in operations and logistics, vehicles and other Physical AI form factors, edge gateways at the boundary, on-premises servers, and across the data center. The agent layer is unified across the entire device-first continuum. An agent that starts a task on the laptop and offloads compute to a gateway, or that runs autonomously on a robot and reports to an enterprise back office, runs through the same runtime, the same identity model, the same observability fabric. The IT and DevOps team operates one control plane across the continuum, not eight different governance regimes for different device classes.

The three interaction modes from the ninth question show up here in concrete form. The offload from the laptop to the gateway is choreography: two peers negotiating in the moment, with no central controller in the loop. The staged rollout described earlier is orchestration: deliberate, top-down, gated. The propagation of identity and trust across the mesh is coordination: peers maintaining a shared operational picture. All three modes run through the same runtime and answer to the same policy, on every device class.

How mimOE closes each of the eight gaps

The eight capabilities the agent layer requires are not abstractions. They are the explicit operational discipline the agent runtime has to deliver. The table below maps each capability to how mimOE provides it.

None of these capabilities replace anything in your existing stack. Every one of them produces an event, an identity, or a policy decision that the existing stack consumes. The agent layer extends the operating model the enterprise already runs. It does not introduce a parallel one.

What mimOE composes with

The agent layer cannot be a silo. Every IT and DevOps team operates a portfolio of tools the agent runtime has to participate in, not replace. mimOE composes with each layer of the existing stack through standard interfaces and explicit integration points.

Flexibility with control

The temptation, faced with an unfamiliar layer of software acting on enterprise systems, is to block it. To declare the agent tools off-limits, to scan for them on managed devices, to mandate that productivity flows only through sanctioned platforms.

That stance loses both ways. The agents stay productive enough that employees will run them anyway, just outside the visibility of the team that tried to block them. The enterprise gives up the productivity gains it could have governed. The same agents that could be operating with identity, scoped authority, and observability operate without any of it. The team that wanted control ends up with less.

The path forward is the path the enterprise already knows from a generation of IT and DevOps practice. Let the productive thing be productive. Make it visible. Make it governed. Make it part of the stack the enterprise already operates. The agent layer makes that possible. It is the same operating model the enterprise has run for users, devices, applications, and code. It just has to be applied where the agent actually executes, with the right unit of management at the right layer.

The agentic AI era will not be defined by which company has the best model or the fastest chip. It will be defined by which enterprises operate that capability with the same discipline they apply to everything else in production. The architecture for that exists. The layer the existing stack was missing now has a name and a place. The control plane reaches the runtime. The agent finally becomes managed.

The post The Agents Are Already on the Laptop. Your IT and DevOps Stack Has to Reach Further. first appeared on mimik.

Three words I wish we’d stop using interchangeably. Cloud architecture treats them as one.

An Agentix-Native System (aka Agentic AI) cannot.

I’ve had this conversation more times than I can count. Someone from a cloud-native background sits down with me, we start talking about Agentix-Native Systems running across distributed devices, and within ten minutes they ask the same question: “Okay, but what orchestrates all of it?”

I get it. If you’ve spent a decade with Kubernetes, Step Functions, or Airflow, orchestration is distributed systems. You don’t have a separate word for it because you never needed one. Everything is orchestrated. The orchestrator is the air you breathe.

In an Agentix system, that assumption falls apart. Not philosophically. Physically. The control plane is too far away, the network is too unreliable, and the thing you’re trying to react to is happening in fifty milliseconds. So I find myself, over and over, having to untangle three words that the cloud world treats as one: orchestration, coordination, and choreography.

Let me make it concrete with a scenario I’ve used a lot lately.

Picture a city that deploys physical AI boxes at five hundred traffic intersections. Each box has cameras, a small NPU, and runs detection models: pedestrians stepping off curbs, cars veering into bike lanes, stalled vehicles, near-misses. Now follow what actually has to happen across that fleet.

Rolling out a new model.

The city’s data team trains a better pedestrian detector. You want it on every box, but staged: ten percent first, watch the metrics, ramp up. That’s a workflow with a predefined shape, a central authority that owns the decision, and tolerable latency to that authority. Nobody’s life depends on the rollout finishing in fifty milliseconds.

That’s orchestration. It’s the right tool. Use it.

I want to be clear about that, because the temptation when you’re pushing back on cloud assumptions is to throw orchestration out entirely. Don’t. Orchestrate where orchestration earns its keep: deployments, lifecycle, the steps inside a single agent’s plan. That’s the conductor’s stand, and it has a job.

Trust.

Box 247 at Main and Elm comes online after a hardware swap. Before any other box trusts what it says (“I just detected a near-miss”), every other box needs to know box 247 is who it claims to be, that its model is the approved version, that it’s authorized to publish incident events.

You can register it centrally, sure. But the agreement across the mesh (who’s online, what they’re running, who can be trusted) has to keep working when the central piece is unreachable. The boxes share that picture among themselves. They gossip identities. They check each other’s signed credentials directly.

That’s coordination. It’s the layer almost everyone overlooks, and it’s the one I find myself spending the most time defending in design reviews.

Coordination doesn’t drive behavior. It makes correct behavior possible.

Without it, your peers can’t act safely on their own. With it, they can.

Actually reacting.

It’s Tuesday, 5:47 PM. Box 247 sees a delivery van veer into the bike lane. The detection fires.

If you tell me the box reports to a controller, the controller decides what to do, then sends commands downstream. I’ll tell you the cyclist is already in the intersection. Two to four hundred milliseconds of round trip is too slow. That path doesn’t exist for this kind of event.

What happens instead: box 247 publishes an event. “Incident, type X, location Y, confidence 0.92.” The two upstream boxes are subscribed to incidents in their geographic neighborhood. They receive it, run their own local logic, extend their red phase by four seconds to slow traffic into the area. The box at the next intersection downstream sees the same event and pulls forward its yellow. Nobody asked permission. Nobody waited for a controller.

That’s choreography. Event-driven, peer-to-peer, no conductor on the floor. The system-level behavior (a whole neighborhood reacting coherently to a near-miss) emerges from each box knowing its own part and reacting to cues.

This is the part that’s hardest to get cloud-native engineers to internalize. There is no central plan being executed here. There’s no one watching from above. And it still works. It works better, in fact, because there’s no controller to be a bottleneck, no controller to be a single point of failure, no controller to be unreachable when the network partitions.

Strategy, operational art, tactics.

If you’ve ever read military doctrine, you’ve seen this pattern under different names. The three classical levels of war are strategy, operations, and tactics.

Strategy is theater-level. The general staff sets objectives, allocates resources, commits to a campaign. Centralized, deliberate, top-down. That’s orchestration.

Tactics is what a squad does when they round a corner and meet contact. No time to call the general. Each soldier knows their drill, reacts to what the soldier next to them does, and the firefight unfolds from the bottom up. That’s choreography.

Operational art sits in between: the level where logistics, intelligence, communications, and command-and-control are coordinated across the theater so that tactical actions add up to strategic objectives. Nobody on the operational staff fires a weapon. They make it possible for the units that do fire to act coherently. That’s coordination.

Strategy, operational art, tactics. Orchestration, coordination, choreography. Same shape, different vocabulary. And the same lesson:

The general cannot issue orders fast enough to win the firefight.

Three modes, three layers.

Step inside a single box. It runs an inference loop: perception, decision, action. But that’s only the inner ring. Wrapped around it, the box is acting at three layers at once. There’s the infrastructure that hosts it on the device. There’s the agent or composed solution it’s executing. There’s the business outcome it ultimately serves. The three modes show up at each one.

Down at the infrastructure, the runtime that turns each device into a node, all three are already in play. Orchestration is the staged rollout of agents and model updates across nodes. Coordination is node identity, discovery, the signed credentials that keep the mesh’s picture consistent when the center is unreachable. Choreography is context driven communication between nodes, peer-to-peer reactions at machine speed, no controller in the loop.

One layer up, where agents compose into actual solutions, the same three show up again wearing different clothes. Orchestration is a single agent’s plan: deterministic, step by step. Coordination is agents discovering each other’s capabilities, agreeing on identity, agreeing on who is authorized to do what. Choreography is event-driven multi-agent behavior where no single agent is in charge and the outcome emerges from each agent reacting to its peers.

One layer up again, at the business outcome the operator actually cares about, the same three again. Orchestration is the supply chain step, the regulatory workflow, the customer journey. Coordination is cross-domain agreement, audit chain integrity, shared state across organizations. Choreography is ecosystem-level behavior where independent businesses react to each other’s events without anyone in the middle conducting.

And the pattern doesn’t stop at one node. Scale out to the fleet and the same three modes run across boxes. Scale further and you have a system of systems, the same three modes at every boundary you cross.

Cloud-native platforms ship orchestration well at every layer and pretend the other two modes don’t exist. An Agentix system has to ship all three, at every layer.

Three rooms.

The orchestrator’s stand for things with a predefined workflow and a central owner. The council chamber for things peers need to agree on. The dance floor for things that have to happen in real time across a mesh that may never have a stable center.

Every Agentix platform I’ve worked on (and every one I’m building now) needs all three. The mistake isn’t picking one. The mistake is letting cloud habits convince you the orchestrator is the only room in the building.

That’s why mimOE is built the way it is. We don’t pick one of the three. We give you a way to live in all three at once.

The post Orchestration, Coordination, Choreography. first appeared on mimik.

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

https://www.businesswire.com/news/home/20260527048436/en/mimik-Launches-mimOE-Studio-to-Accelerate-Agentix-Native-Systems-with-Sustainable-Economics-and-Scalable-Growth

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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.

The applications that defined the last decade of computing were static and siloed. Cloud-first architecture was the natural design choice for that world. The device was a display. The cloud was the brain.

Agentic systems are different in nature, not just in degree. Agents must have autonomy in execution. They must run on devices, at the edge, on premises, and in the cloud simultaneously. They must be able to discover each other dynamically, make decisions in real time, and act directly on data at the point where it is generated. Forcing this model through a cloud-first architecture introduces latency, dependency, and sovereignty gaps that simply did not exist in the static app world.

Yet today, the conversation about sovereign AI is still using the vocabulary and mental model of that older world. In a world where agents think, decide, and act autonomously at the point where data is born, data residency alone does not constitute sovereignty. It is simply the address on the building.

What Sovereignty Actually Means

Sovereignty is the full right and power to govern oneself, make decisions, take action, and persist under one’s own authority, where permission is self-defined and self-governed at the point of execution based on sovereign policies. Four elements must be present simultaneously at the point of execution: authority, control, continuity, and minimum intermediaries on the data path. Remove any one of them and what remains is not sovereignty. It is managed dependence.

Think about how you operate across the full spectrum of your daily activities. In a board meeting you communicate, present, and carry yourself differently than you do with a colleague over lunch, or on a client call, or in a personal conversation. You shift between these contexts fluidly, in real time, sometimes managing several simultaneously. You do not route these decisions through a central authority. You carry your context, your authority, and your boundaries with you and execute instinctively at the point of each interaction. That is sovereignty in execution. Agentix-native systems must be designed to mimic exactly this behavior, deliberately, at every layer, and at machine speed.

Every intermediary on the path between a decision and its execution is a point where sovereignty is conditionally suspended. Any intermediary is a dependency. A chain of intermediaries is a chain of vulnerabilities. This is the condition that current AI architectures routinely violate, including those being marketed as sovereign. The lack of sovereignty in execution is one of the primary reasons AI adoption at scale remains slower than the market expects.

The Five Dimensions of Sovereignty

1. Data Sovereignty: Where Data Governance Starts

Location alone does not tell you who opened the file, who copied it, or who made a decision based on it. It answers one question and leaves four others entirely open.

This means: the agent must be able to process, structure, and enrich data at the point where it is generated, whether on a sensor, a camera, or an industrial machine, and route each scope to the appropriate destinations based on storage and sovereignty policy executed at the source.

2. Compute Sovereignty: Where the Thinking Happens

An AI system that stores data locally but executes inference in an external cloud controls only where the paperwork is filed when the answer comes back. The data has an address. The thinking does not.

This means: the agent must have the autonomy to execute inference at the point of action, within its runtime context and sovereignty policy, without the necessity of a round trip to a remote model or cloud service.

3. Coordination Sovereignty: Who Collaborates and on What Terms

Collaboration is contextual, permission-driven, and scoped to relevance. It must happen directly between the parties who need to work together without routing every exchange through a single gatekeeper who holds everything.

This means: the agent must be able to determine and exercise its authority to collaborate with other agents within its runtime context, dynamically at the point of interaction. This enables autonomous collaboration dynamically choreographed within the bounds of security, access and authorization scope to execute in full operational sovereignty.

4. Operational Sovereignty: The Right to Continue Acting

When the person at the top of the chain is unreachable, the team does not shut down. They operate within their last known mandate until contact is restored. When an AI system loses its cloud connection, it stops. That is not a resilience problem. It is a sovereignty problem.

This means: the agent must be able to determine and exercise its authority to collaborate with other agents within its runtime context, dynamically at the point of interaction, carrying the ontological context it needs to act without reaching back to a central system for meaning. This enables autonomous collaboration dynamically choreographed within the bounds of security, access and authorization scope to execute in full operational sovereignty.

5. Outcome Sovereignty: Who Owns the Result

If the signature on a deliverable must happen on someone else’s platform, using someone else’s ledger, the outcome has a dependency built into it regardless of how the work leading up to it was conducted.

This means: the agent must be able to settle the results of its actions, record outcomes, and maintain an audit trail with full observability of every execution, operation, and interaction from the source, within its own runtime environment, under the organization’s full control.

Embedded Sovereignty: Where all 5 Dimensions Converge

Embedded Sovereignty is a dedicated execution layer present at the point of origin, delivered in three forms:

as part of an agentix solution or application as Sovereignty-in-solution, pre-integrated on a device as Sovereignty-on-Device, or embedded at the chip level as Sovereignty-on-Chip. The closer it is to the chip, the faster the path to production for the entire ecosystem: device vendors, developers, and enterprises alike. Without it, each of the five dimensions requires a separate implementation effort. Data residency requires infrastructure decisions. Compute sovereignty requires on-device inference capability. Coordination sovereignty requires a distributed agent runtime. Operational sovereignty requires offline-first architecture. Outcome sovereignty requires on-device settlement capability.

Addressed individually, each is a project. Addressed sequentially, they compound into years of engineering debt. From the moment a system with Embedded Sovereignty powers on, connected or not, all five dimensions are already satisfied. That is the difference between sovereignty as a destination and sovereignty as a design principle. That design principle is also what distinguishes Embedded Sovereignty from an operating system. An operating system is passive by design. It manages resources and waits for instructions. Agents do not wait. They collaborate with other agents in dynamic workflows. Operating based on the principles of follow, observe, respond, and learn, they discover, decide, coordinate, and act proactively based on context, authority, and sovereign policy. What they require is a Sovereign Execution System, one that is device-first by design, as autonomous and context-aware as the agents running within it, and capable of operating across the full compute continuum from the chip to the cloud.

For the enterprise, Embedded Sovereignty is not an infrastructure decision. It is the difference between AI that operates under your authority and AI that operates under someone else’s.

Without it, every jurisdiction becomes a re-engineering project, every cloud dependency becomes a point of operational risk, and every sovereignty gap accumulates into compliance and liability exposure. With it, the organization deploys once, governs from the point of execution, and retains full control across every context in which its agents operate. For device vendors and OEMs, every device that ships becomes a production-ready sovereign node out of the box. For developers, the execution environment travels with the agent across any hardware, any OS, and any connectivity state.

This is not a future capability. mimik delivers Embedded Sovereignty as a production-ready platform, available today at the solution, device, and chip level.

The post Sovereignty in Execution Goes Beyond Data Residency 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.