openEuler Redesigns OS Architecture to Support Autonomous AI Agents

Conventional operating systems focus on managing computational resources—CPU scheduling, memory allocation, storage, networking, and running applications. Agentic artificial intelligence adds a new dimension to this challenge: systems must now handle the complete lifecycle of intelligent agents, encompassing deployment, task scheduling, execution isolation, context management, memory retrieval, tool invocation, observability, and recovery.

openEuler is responding to this shift by redesigning its operating system capabilities to facilitate the development, deployment, execution, coordination, and optimization of agents across heterogeneous computing environments. The initiative is being showcased at the Agentic Infra OS Conference & openEuler Summit 2026.

AI Agents Need More Than Computing Power

Modern AI agents are advancing beyond text generation to execute code, invoke tools, interact with applications, and manage multi-step workflows. When these workloads expand, infrastructure encounters fresh obstacles: secure execution, rapid environment setup, efficient context reuse, durable memory, and flexible resource allocation.

Existing infrastructure was not built with these demands in mind. The next phase of operating system development must transcend basic process and hardware resource management to encompass the entire agent execution lifecycle.

openEuler's agentic operating system strategy centers on constructing an open, secure, and performant software stack that integrates operating system capabilities with agent runtimes, AI frameworks, data services, and heterogeneous computing resources.

The infrastructure operates across multiple computing architectures, spanning general-purpose processors including x86, Arm, RISC-V, and LoongArch, as well as AI accelerators from various vendors. This multi-architecture strategy is critical because enterprise AI implementations typically span multiple hardware architectures and accelerator platforms.

The design is organized into two primary layers: Agent Kernel, delivering core system-level capabilities, and Agent Service, offering reusable functions such as secure sandboxing, memory, session management, and Tool Gateway. Supporting this foundation are complementary infrastructure systems—including distributed caching and serverless data services—that address the performance, scalability, and data handling needs of agent workloads.

Rather than consolidating all capabilities within the OS kernel, this model merges system-level optimization with reusable infrastructure services and an open ecosystem. Five technical pillars form the backbone of this architecture.

Five Pillars of Agentic Infrastructure

1. Sandbox: Secure, Scalable Agent Execution

Agents require isolated environments to run generated code, call tools, and connect with external systems. These environments must launch rapidly, accommodate high concurrent loads, and recover reliably when tasks fail.

openEuler combines lightweight virtualization, snapshot-based recovery, resource prewarming, and high-density scheduling to meet these needs. Technologies such as StratoVirt and the Conch runtime facilitate sandbox creation and management, while orchestration features enable large-scale rollout.

openEuler's benchmarks show sandbox startup latency as low as 20 ms, supporting more than 2,000 sandboxes. In another high-concurrency test, the system provisioned 100,000 sandboxes in 10 seconds. These metrics illustrate the capability for rapid provisioning and dense agent execution at enterprise scale.

2. KV Cache: Making Inference Context Reusable

In iterative agent workflows, repeated inference frequently involves significant context overlap. Recalculating or repeatedly moving identical attention states consumes accelerator memory and increases time to first token (TTFT).

openEuler's KV cache strategy integrates multi-tier caching, global indexing, NUMA-aware placement, and rapid cross-node data transfer. Technologies including LMCache and openYuanrong enable KV cache sharing across instances and placement closer to compute.

Agent-aware scheduling extends this capability: by aligning agent context to KV cache chunks, the platform can anticipate reuse and actively coordinate prefetching and eviction.

The results are substantial: inference throughput rises by 1.5x–3x, while time to first token (TTFT) decreases by 30%–70% in specific use cases.

3. Memory Agent: Giving Agents Persistent Context

KV cache optimizes inference through computed state reuse. Agent memory tackles a separate issue: enabling agents to preserve and access useful information across multiple turns, tasks, and sessions.

openEuler's memory system combines memory extraction and orchestration frameworks with hybrid retrieval and storage options. Tools including OpenViking, oGMemory, and openGauss enable structured context management, vector and full-text retrieval, and knowledge distribution among multiple agents.

This allows agents to access relevant information on demand rather than reprocessing extensive conversation histories. Multi-agent workflows also benefit from shared knowledge accumulation.

By fetching only the context an agent requires, the system can lower token costs by 30%–70% and boost task success rates by 1.5×–3× in specific scenarios.

4. Serverless Database: Enabling Elastic Data Services for AI Agents

Beyond the Agent Kernel and Agent Service layers, data infrastructure plays a crucial supporting role in agentic workloads. Serverless database features deliver the elastic, branchable data environments required by agents that generate, modify, compare, and reverse data across evolving workflows.

Built on openGauss, this approach merges elastic compute provisioning with copy-on-write storage and database branching. Multiple branches can share common baseline data while maintaining separate modifications. Point-in-time recovery, branch comparison, and merging capabilities further support adaptable data workflows. The outcome is a more flexible data foundation for AI agents, where database environments can be created, adjusted, and restored as workflows progress.

The system targets rapid, elastic data provisioning, with millisecond-level instance startup and second-level database branch creation and rollback. These features, powered by compute elasticity and copy-on-write storage, allow agents to establish and oversee isolated data environments with minimal resource overhead.

5. An Open, Heterogeneous Ecosystem

Agentic infrastructure cannot rely on a single model, framework, or hardware platform. It must integrate diverse components while keeping their resources manageable and accessible.

openEuler's ecosystem combines agent frameworks, inference engines, orchestration platforms, memory systems, and heterogeneous processors and accelerators. Through partnerships with projects and technologies such as Kubernetes, openFuyao, vLLM, MindSpore, and openGauss, it seeks to support varied agent workloads across different computing environments.

The goal extends beyond mere compatibility. The aim is to establish a unified infrastructure base where developers and ecosystem participants can construct, launch, and expand agent applications.

From an Operating System to an Agentic Infrastructure Foundation

The shift toward Agentic AI represents more than an incremental change in application development. It is fundamentally altering the infrastructure demands of enterprise computing.

As agents grow more autonomous, infrastructure must deliver more than model inference. It must enable secure execution, efficient data movement, persistent memory, elastic data services, and dependable coordination across increasingly sophisticated workloads.

openEuler's agentic infrastructure program tackles these needs through five interconnected technology domains: an open infrastructure vision, high-performance sandbox execution, distributed KV cache acceleration, persistent agent memory, and serverless databases.

Each addresses a distinct challenge in agent deployment and execution. Collectively, they demonstrate an approach to making agent capabilities more scalable, reusable, and manageable across heterogeneous computing environments.

• Sandbox: Secure and recoverable agent execution.
• KV Cache: Efficient reuse of inference context.
• Memory Agent: Persistent context and knowledge retrieval.
• Serverless Data Services: Elastic and branchable data infrastructure.
• Open Ecosystem: Integration across AI frameworks, orchestration platforms, and heterogeneous hardware.

Together, these components signal a broader transformation in the operating system's role. As AI agents become more autonomous and workloads grow more dynamic, infrastructure must transcend compute resource allocation: it must help agents execute securely, operate efficiently, maintain context, and scale reliably.

This is the infrastructure trajectory openEuler is pursuing through agentic infra OS, establishing an open foundation for the next wave of intelligent enterprise computing.