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PT Mitra Integrasi Informatika (MII), together with PT Synnex Metrodata Indonesia, received the Best Commercial Partner 2025 award from Fortinet in recognition of the highest revenue achievement in the Commercial segment.This achievement reflects MII’s success in delivering Fortinet cybersecurity solutions across various industries, particularly manufacturing, hospitality & tourism, agriculture & forestry, healthcare, and mining & metals.Supported by MII’s implementation capabilities and Fortinet’s technology, customers can leverage solutions such as Next-Generation Firewall, Multi-Factor Authentication (MFA), and Email Security to strengthen cybersecurity, enhance protection against digital threats, and maintain operational continuity.This award is not only a significant achievement for MII but also serves as an inspiration to continue helping more organizations build stronger cyber resilience.Interested in learning more about MII's technology solutions and services?Contact our team to discover the right solutions tailored to your business needs.Connect Wtih MII
PT Mitra Integrasi Informatika (MII) received the Strategic Partner of the Year Award from ManageEngine during the ManageEngine Partners' Appreciation Night, held as part of UserConf Southeast Asia 2026 on Wednesday, July 8, at The Ritz-Carlton Jakarta, Pacific Place.Award RecipientPriadi – Division Manager, PT Mitra Integrasi Informatika (MII)Presented byPromoth Kumar – Channels, ManageEngineThe award recognizes MII's commitment as a strategic partner in delivering ManageEngine solutions, including IT Service Management, Unified Endpoint Management, IT Operations, Identity & Access Management, and Network & Security Management. Through these solutions, MII helps organizations manage their IT operations more efficiently, securely, and seamlessly.Interested in learning more about MII's technology solutions and services?Contact our team to discover the right solutions tailored to your business needs.Connect Wtih MII
PT Mitra Integrasi Informatika (MII) has been honored with the Partner of the Year FY2026 award from Vectra AI in recognition of its strong collaboration and continued business growth.Together with Vectra AI, MII delivers Network Detection and Response (NDR) and Managed Detection and Response (MDR) solutions that help organizations proactively detect cyber threats while accelerating security investigation and incident response.Demonstrating its technical expertise, MII has successfully implemented an NDR solution for a customer in the financial services industry. The solution was integrated with the customer's Extended Detection and Response (XDR) platform and firewall infrastructure, providing enhanced protection for thousands of users across a complex IT environment.This recognition reinforces MII's commitment to strengthening its partnership with Vectra AI and delivering innovative cybersecurity solutions that help organizations build greater cyber resilience.Interested in learning more about MII's technology solutions and services?Contact our team to discover the right solutions tailored to your business needs.Connect with MII
Start with what an agent is actually doingA chatbot answers. An agent acts.Ask a chatbot a question and it returns text. Give an AI agent a task like "analyze this spreadsheet and chart the outliers," "fix the failing test in this repo," or "open this site and pull the three cheapest listings," and it must take action. It runs code, drives a browser, writes files, reads the results, decides what to do next, and repeats the process until the task is complete.That difference creates a requirement chatbots never had: a workspace. Agents need somewhere to execute code, store temporary files, access a browser, and perform the work itself.The obvious answer might be to give agents the same kind of environment developers use—a persistent laptop or VM. In practice, that's exactly the wrong model. Understanding why explains what Alibaba Cloud AgentRun is designed to provide.The default flipThink about your own development environment. You install tools once, leave files where they are, and return later to find everything unchanged. For humans, persistence is the default, while isolation is only used when working with untrusted software.Agents need the opposite: isolation by default, persistence by exception. Every task should begin in a fresh, sealed workspace, while anything that must survive—such as memory, identity, permissions, artifacts, or audit logs—is stored outside that workspace.This separation is the key architectural shift. The execution environment remains disposable, while everything valuable is deliberately kept durable elsewhere.Seen at the level of the execution workspace, the contrast between humans and agents becomes clear: A human's dev environmentAn agent'sexecution workspaceLifespanMonths. You return to it.One task,seconds to minutes, then gone.What persists in itEverything, by default.Nothing, by default. Durable state lives outside it.IdentityThe machine is yours.The workspace is anonymous; identity and permissions are attached per task, from outside.ProvisioningSet up once, cost amortized.Materialize on demand, vanish when done.ConcurrencyOne human, one machine.Thousands firingat once, then none.TrustYou trust yourself.Untrusted by construction. It runs code it wrote, on inputs (web pages,documents) that can hide instructions.This is the gap AgentRun is designed to fill. Instead of provisioning and maintaining servers for agent execution, AgentRun creates a fresh, isolated workspace whenever a task starts, destroys it when the work is finished, and charges only for the time the workspace is actually running.The rest of this article explores why that model is a better fit for AI agents than long-lived infrastructure.Why disposable execution is actually better, and how AgentRun delivers itAt first glance, disposable may sound like a compromise. For AI agents, it's actually an advantage. Each benefit maps directly to the way AgentRun manages execution environments.1. A guaranteed clean slateEvery task begins from the same known-good baseline. No leftover files, dependency drift, or hidden state from previous runs can affect the outcome. AgentRun provisions a fresh sandbox for every task and removes it once it's no longer needed, making execution far more consistent and reproducible.2. A bounded blast radiusAgents execute code they generate themselves while interacting with untrusted inputs such as websites and documents. If something goes wrong—whether a faulty command, malicious content, or a filled disk—the impact is confined to that isolated workspace, which is automatically destroyed after use. AgentRun further limits risk by isolating each sandbox and enforcing a maximum lifetime.3. No cross-task contaminationPersistent workspaces can accidentally carry over credentials, cached data, or temporary files from one task to another. By creating a new sandbox for every execution, AgentRun eliminates this entire class of risk by design.4. Pay only for work performedUnlike human development environments, agents spend much of their time waiting—for model responses, browser events, or external services. Keeping dedicated machines running would waste resources. Built on Alibaba Cloud Function Compute, AgentRun creates workspaces only when needed, so costs scale with actual execution instead of idle time.5. Elastic scaling without infrastructure managementAgent workloads are highly variable. Thousands of tasks may start simultaneously and finish minutes later. Because workspaces are created on demand and removed automatically, AgentRun scales with demand without requiring capacity planning, server maintenance, or idle infrastructure.Together, these characteristics align with the agent threat model. AI agents routinely execute code and interact with untrusted content, making isolation and disposability fundamental design choices rather than optional optimizations.So that's how agents workThe workspace reveals how AI agents actually operate.An agent isn't an oracle that simply knows the answer. It works through a loop: planning, taking action, observing the result, and trying again until the task succeeds. That process inevitably creates temporary files, failed attempts, and discarded outputs, which is exactly why execution belongs inside a disposable workspace.Anything worth keeping—such as memory, generated artifacts, or execution history—belongs outside that workspace. The execution environment is temporary; what the agent learns and produces is not.Picture an AI agent as a tireless intern given a sealed room, a computer, and one assignment. After the task is complete, the room is cleared and rebuilt for the next job, while the notes it produced are safely archived. That's more than a metaphor—it's a close approximation of the architecture.One concrete task, start to finishConsider a sales operations agent asked to "pull last week's pipeline report and flag stalled deals."A fresh workspace is created. The agent opens a browser, signs in to the CRM, downloads the report, runs a short Python script to identify opportunities untouched for 14 days, writes a summary, and exits. Once the task is complete, the workspace is destroyed.Just as important is what doesn't persist:The browser session isn't left logged in.Temporary packages and dependencies don't remain on a long-lived machine.CRM credentials aren't stored on an always-on virtual machine.Every execution starts from the same clean environment, eliminating configuration drift.Meanwhile, the outputs that matter—the report, audit trail, and memory that "Acme has gone quiet"—are stored outside the workspace, allowing execution to disappear while preserving everything of lasting value.That single workflow captures the architectural shift: execution is disposable, while memory, artifacts, and governance remain durable.Meet AgentRun: the flip, as a managed serviceThis is exactly what AgentRun delivers. Rather than asking you to provision and manage infrastructure for AI agents, AgentRun provides the execution environment as a managed service while keeping each architectural layer clearly separated.Getting started is simple: define your agent's model, prompt, and tools, and AgentRun handles the underlying execution environment.The execution sandboxAt the center is the AIO Sandbox—the disposable workspace where agents perform their tasks.It combines the three capabilities an agent needs into a single isolated environment:A headless browser for interacting with websitesA code interpreter for executing scriptsAn interactive terminal with a filesystem for managing files and commandsAlibaba Cloud aptly describes these as an agent's eyes, brain, and hands.Creating an AIO Sandbox: choose your resources, browser, and runtime. The workspace is created on demand and automatically removed when the task is complete.Browser SandboxFor browser automation, AgentRun provides a dedicated Browser Sandbox.It supports standard tools such as Playwright and Puppeteer through the Chrome DevTools Protocol, while a built-in VNC live view lets you watch the agent navigate websites in real time. This visibility makes debugging significantly easier by showing exactly what the agent is doing instead of treating browser automation as a black box.Model governanceAgentRun also provides centralized control over model access.Administrators can define which models agents are allowed to use, apply usage limits, and enforce governance policies across deployments.Memory and stateThe agent's long-term memory is deliberately separated from its execution environment.Because memory, state, and generated artifacts are stored outside the workspace, every execution can start from a clean environment without sacrificing continuity between tasks.Observability and control Every execution is tracked through an observability layer that records agent activity, execution traces, and resource usage.Together, these components allow AgentRun to separate disposable execution from durable state while presenting everything through a single managed platform.But is it safe to run AI-written code in the cloud?A natural question follows: if AI agents execute untrusted code on shared cloud infrastructure, how is that kept secure?The answer isn't that sandboxing makes the problem disappear. Instead, AgentRun applies defense in depth.Each workspace runs inside a strongly isolated execution environment based on the same lightweight VM-class isolation used to secure large-scale multi-tenant serverless workloads. While no isolation technology is perfect, this provides a mature and battle-tested security boundary.At the same time, AI agents introduce a different class of risk that sandboxing alone cannot solve: prompt injection. Instructions hidden inside web pages or documents can influence an agent's behavior even when the execution environment itself remains isolated.That's why security relies on multiple complementary controls:Strong workload isolationDisposable execution environmentsControlled network egressLeast-privilege credentialsDisposability isn't the entire security model, but it significantly limits the impact of mistakes by ensuring compromised workspaces disappear instead of becoming long-lived liabilities.Why "on Function Compute" mattersThe infrastructure model is just as important as the security model.Because AgentRun runs on Alibaba Cloud Function Compute, workspaces are created only when tasks begin and released automatically when they become idle. Each workspace has a limited lifetime, eliminating the need to reserve capacity, manage virtual machines, or remember to shut anything down.This serverless architecture aligns naturally with agent workloads, which are highly elastic and spend much of their time waiting for model responses, browser actions, or external services. Instead of paying for always-on infrastructure, you pay only while the agent is actively working.What to take from thisIf you're building AI agents, think less about servers and more about separating disposable execution from durable state.Agent-written code shouldn't share infrastructure with production applications, but neither should it require a fleet of dedicated virtual machines. AgentRun provides disposable execution as a managed service, while integrating memory, governance, and observability around it.More broadly, this architecture reflects a shift taking place across the industry. In much the same way containers became the default deployment model for modern applications, disposable execution environments are becoming the natural foundation for agent workloads.The agents themselves may capture the attention, but the workspace they run in is what makes them practical. By isolating execution, preserving only what matters, and discarding everything else, disposable workspaces enable AI agents to operate safely, consistently, and at scale. Source: AlibabaCloud
Artificial Intelligence (AI) is rapidly transforming the way organisations operate, make decisions, and deliver value to customers. From AI-powered assistants and recommendation engines to intelligent search and predictive analytics, businesses are embracing AI to improve productivity and unlock new opportunities. However, while much of the attention is focused on Large Language Models (LLMs), the true foundation of every successful AI initiative lies in its data platform.As organisations accelerate AI adoption, PostgreSQL has evolved beyond a traditional relational database into a modern AI data platform. Its ability to manage transactional data, semi-structured data, and vector embeddings within a unified ecosystem makes it an increasingly attractive choice for enterprises seeking to simplify their AI architecture while maintaining the reliability and governance expected from enterprise databases.One of the key enablers of modern AI applications is vector search. Unlike traditional keyword-based searches, vector search uses embeddings—numerical representations of text, images, or other data types—to retrieve information based on semantic meaning rather than exact matches. This capability is fundamental to Retrieval-Augmented Generation (RAG), intelligent knowledge management, enterprise search, AI-powered customer support, and many other generative AI use cases.Rather than introducing a separate vector database into the technology stack, organisations can leverage PostgreSQL's vector capabilities to keep operational data and AI-ready data within the same platform. This approach reduces architectural complexity, eliminates unnecessary data duplication, and simplifies governance by maintaining a single source of truth for both transactional and AI workloads.However, building an AI data platform requires careful architectural decisions. One of the most important considerations is data sovereignty. Many organisations—particularly those operating in banking, financial services, government, healthcare, and telecommunications—must comply with regulations that require sensitive information to remain within controlled environments. Selecting a platform that supports deployment across on-premises, private cloud, public cloud, or hybrid environments enables organisations to adopt AI while meeting regulatory and security requirements.Another critical factor is latency. AI applications often require real-time responses, whether for fraud detection, personalised recommendations, or conversational AI. If transactional data, vector databases, and AI models reside in different environments, every interaction introduces additional network latency, data synchronisation challenges, and operational overhead. Consolidating data and vector capabilities within PostgreSQL can significantly reduce these bottlenecks, resulting in faster response times and a more streamlined architecture.Running AI workloads alongside traditional transactional workloads also presents operational challenges. AI processes such as embedding generation, vector similarity searches, and inference place different demands on system resources compared to conventional online transaction processing (OLTP). Without proper planning, resource-intensive AI workloads can impact the performance of mission-critical business applications. Organisations should therefore consider workload management strategies, indexing, performance monitoring, replication, high availability, backup, and disaster recovery to ensure both workloads coexist efficiently without compromising business continuity.Beyond performance considerations, organisations should also pay close attention to data quality, governance, security, and scalability. AI models are only as effective as the data they consume. Poor-quality data, inconsistent governance, or inadequate access controls can significantly reduce the accuracy and trustworthiness of AI-generated insights. Likewise, as AI adoption expands, the data platform must be capable of scaling seamlessly while maintaining performance, resilience, and operational efficiency.As an enterprise-grade PostgreSQL solution, EnterpriseDB (EDB) provides the capabilities organisations need to build and operate AI-ready data platforms with confidence. Through enterprise-grade security, high availability, advanced replication, monitoring, and professional support, EDB enables businesses to modernise their data infrastructure while supporting both transactional and AI-driven applications within a single, integrated platform.As highlighted in the Metrodata Solution Day (MSD) 2026 theme—"Winning with AI: Build, Run, and Scale for Measurable Impact"—successful AI initiatives begin with a robust and well-designed data foundation. PostgreSQL is no longer just a database for transactional applications; it has become a strategic platform for enterprise AI. By making the right architectural decisions and understanding the trade-offs around vectors, data sovereignty, latency, and operational workloads, organisations can build AI solutions that are secure, scalable, and capable of delivering measurable business value.Interested in learning more about MII's technology solutions and services?Contact our team to discover the right solutions tailored to your business needs.Connect Wtih MII
Artificial Intelligence is only as powerful as the data behind it. However, as enterprise data continues to grow at an unprecedented rate, the biggest challenge is no longer simply storing data—it is ensuring that data remains accessible, protected, and ready to fuel innovation.Today, organizations manage billions of data objects generated from AI training, analytics, backups, media content, IoT, and cloud-native applications. Traditional storage infrastructures are no longer designed to handle this scale and complexity. What is needed is an object storage platform that can grow as fast as the business does.This is where NetApp StorageGRID makes a difference.StorageGRID is more than just object storage. It is a modern data foundation that enables organizations to build petabyte- to exabyte-scale data lakes without compromising performance, security, or operational efficiency. With its scale-out architecture, capacity and performance can be increased linearly without downtime or complex data migrations.What makes StorageGRID even more relevant in the AI era is its support for the Amazon S3 API, enabling various AI frameworks, analytics platforms, and cloud-native applications to access data through a widely adopted industry standard. Data becomes easier to integrate, faster to utilize, and better prepared to generate valuable insights.At the same time, the growing threat of ransomware and increasingly stringent regulations make data security a top priority. StorageGRID provides Object Lock, Information Lifecycle Management (ILM), geo-replication, and automated policies to ensure that data remains protected, available, and efficiently managed throughout its lifecycle.The question organizations should no longer be asking is, "How much storage do we need today?" Instead, they should be asking, "Is our data infrastructure ready to meet the demands of AI five years from now?"Ultimately, the success of digital transformation is not determined by how much data an organization has, but by how quickly it can turn that data into business value.With NetApp StorageGRID, organizations gain more than just storage capacity. They gain a scalable, cyber-resilient, and AI-ready data platform—a foundation that enables innovation to grow without limits.The future belongs to organizations that can scale their data as fast as they scale their ambitions.Interested in building a data infrastructure that can keep pace with the growth of AI and object storage?Kevin KurniawanKevin.kurniawan@mii.co.id
