【China Daily】AI agents are emerging as a key direction in the evolution of artificial intelligence, increasingly serving as the core medium for human–machine collaboration and autonomous decision-making. They are widely expected to become foundational building blocks of future intelligent systems. However, the infrastructure supporting agent deployment remains largely dependent on extensive “glue code” to stitch together fragmented components. This often leads to systemic inefficiencies: valuable compute resources remain underutilized, unexpected failures can interrupt large-scale training jobs, and operations teams are burdened with continuous alerts and troubleshooting. As a result, traditional toolchains and human-centered DevOps practices are increasingly insufficient for the dynamic and complex nature of agent production workloads.
We believe that today’s agent infrastructure requires a fundamental paradigm shift—one in which the infrastructure system itself is endowed with capabilities for autonomous decision-making, coordination, and continuous evolution. By leveraging the autonomy and intelligence of agents, infrastructure systems can make decisions that are more efficient, accurate, and adaptive than manual human operations, taking over complex tasks that previously relied on expert-level cognitive labor. This enables a form of operational excellence that goes beyond accumulated human experience, ultimately supporting more efficient, stable, and accessible agent innovation.
Today, Infinigence formally introduces the Infrastructure Agent Swarm, a next-generation intelligent infrastructure system built upon its long-term development of AI-native infrastructure and extensive operational experience. The system integrates multi-agent collaboration architectures with real-world industrial requirements to provide a unified solution for infrastructure intelligence. The Infrastructure Agent Swarm encapsulates a set of specialized agent modules, including SOTA model selection, infrastructure platform management, resource orchestration, troubleshooting, and intelligent cluster operations. Together, these agents form a highly autonomous and dynamically coordinated system that enables closed-loop intelligence across the full lifecycle of infrastructure management—spanning perception, decision-making, and execution. As a result, the system significantly improves resource utilization, operational efficiency, and overall system reliability, enabling an order-of-magnitude expansion in infrastructure management capability without proportional increases in human labor or operational cost.
Taking traditional intelligent computing cluster operation and maintenance as a reference, the Infinigence Infrastructure Agent Swarm unifies fragmented workflows that were previously distributed across development, operations, and maintenance teams into a single closed-loop system of “perception–decision–execution” through multi-agent collaboration. Whether it is elastic scheduling of pooled compute resources, unified cross-region cluster management, or coordinated control of high-performance networking, storage, and security systems, the swarm architecture enables continuous optimization and adaptive reconfiguration. This transforms the compute platform from a passive resource provider into an autonomous system that proactively aligns infrastructure behavior with R&D tasks and business objectives, leading to significant improvements in resource utilization, energy efficiency, and system reliability.
Within the swarm, specialized agents assume clearly defined roles. The SOTA model selection agent acts as a “technical sentinel,” continuously monitoring emerging model capabilities and system requirements to automatically match optimal models and execution environments for different workloads, thereby eliminating inefficient over-provisioning of compute resources.
The infrastructure platform manager agent functions as the system’s operational orchestrator, responsible for environment initialization, container orchestration, quota management, and security policy enforcement across the IaaS and PaaS layers. By interpreting user intent at the task level, it automates complex infrastructure procedures—for example, automatically provisioning distributed container clusters and data caching layers for RLHF training workloads.
The resource operations agent adopts a cost-and-performance optimization perspective. It continuously evaluates compute utilization, queue latency, energy consumption, and pricing models in real time, and dynamically orchestrates resource pools to achieve an optimal balance between supply and demand. From an operator’s standpoint, this ensures that GPU resources are no longer left idle for extended periods, while also mitigating resource contention during peak demand periods.
At the operations layer, the troubleshooting agent and the intelligent cluster operations agent form a complementary “front-end + back-end” architecture. The troubleshooting agent serves as the primary interface for users and operations teams, exposing a natural-language entry point that can rapidly provide diagnostic suggestions or automatically trigger remediation workflows. In contrast, the cluster operations agent functions as a deep system-level diagnostician, performing root cause analysis and automated recovery by correlating logs, monitoring signals, and trace data. It can further anticipate potential failures before task execution and proactively mitigate risks.
The Infrastructure Agent Swarm represents Infinigence’s concrete implementation of the emerging AI infrastructure paradigm known as Agentic Infra. By placing autonomous agents at the core of system design, Agentic Infra fundamentally reshapes the traditional layered and loosely coupled stack of IaaS, PaaS, MaaS, and application-level agent systems. Instead of a vertically fragmented pipeline, it enables a tightly integrated, closed-loop architecture in which infrastructure and intelligence are co-designed and continuously co-optimized.
This architecture unifies heterogeneous compute resources, cloud-native components, and AI platform capabilities into a single addressable space for agents. Built on the Infinigence Infrastructure Agent Swarm, it enables autonomous task decomposition and dynamic orchestration of compute resources, models, tools, and external APIs across execution workflows, while providing end-to-end capabilities for execution, monitoring, and troubleshooting. The system spans the full lifecycle of intelligent production, from compute adaptation and model selection to security governance and final deployment. This effectively enables an automated, end-to-end production paradigm in which complex infrastructure workflows can be triggered and completed through natural-language instructions—realizing a “one command, one agent” model, and significantly lowering the barrier for building and operating AI systems.
Over time, Infinigence has accumulated extensive experience serving universities, research institutions, and enterprise customers, establishing deep expertise in heterogeneous compute management, scheduling, training, and inference optimization. This has enabled the development of a comprehensive end-to-end technology stack for commercial AI infrastructure deployment, including large-scale model performance optimization, distributed training acceleration, reinforcement learning frameworks, multi-tenant heterogeneous scheduling, RDMA network topology optimization, high-performance storage systems, and GPU cluster operation and maintenance. Together, these capabilities form a full-stack technical foundation that allows intelligent agents to be more seamlessly integrated into real-world production environments.
Currently, through close collaboration and iterative refinement with leading text-to-text and text-to-image agent clients, Infinigence’s Infrastructure Agent Swarm has been deployed in multiple real-world business workflows, demonstrating stable and tangible results in practice.
With over one million monthly active users, NieTa is widely regarded as a creative haven for anime and manga enthusiasts, enabling users to generate characters and stories through AI and freely express themselves in stylized, character-driven forms. Founder and CEO Hu Xiuhan noted: “In traditional agent development, over 30% of our resources were spent on refactoring generic components and maintaining workflows. After partnering with Infinigence, end-to-end automated scheduling and resource orchestration significantly reduced our efforts in compute adaptation, model integration, and secure deployment, while improving overall iteration speed by approximately fivefold.” He further added that agent development is shifting from a labor-intensive, engineering-heavy process toward a goal-driven paradigm where execution is increasingly handled autonomously by systems.
As a leading innovator in youth social platforms, Soul App has evolved from early-stage social connection products to the creation of AI bots, and further toward end-to-end multimodal capabilities spanning dialogue, voice, vision, and virtual humans. Through this progression, it continues to redefine the boundaries of human–machine interaction with AI technologies. Zhang Lu, founder and CEO of Soul App, said: “Through our joint refinement process, Infinigence’s Infrastructure Agent Swarm has significantly compressed our innovation cycles and greatly reduced the cost of experimentation. Ideas that were previously shelved due to technical barriers or resource constraints can now be rapidly brought to life. We believe this launch is not merely an upgrade at the tooling level, but the beginning of an ecosystem-level transformation.”
Infinigence co-founder and CEO Xia Lixue once outlined the company’s core vision for AI-native infrastructure at a public event: “Before turning on the faucet, we don’t need to know which river the water comes from. Likewise, in the future, when we use various AI applications, we won’t need to know which foundation models are being called or which accelerator chips are providing the compute.”
The next-generation Agentic Infra paradigm proposed by Infinigence is rooted in this philosophy — extending beyond the seamless abstraction of “M (models) × N (chips)” to a fully automated and intelligent pipeline that spans from idea to deployment.
In an era where intelligent agents are becoming ubiquitous, Infinigence aims to enable every enterprise to participate in this transformation—particularly small and medium-sized teams with strong domain expertise—by lowering the barriers and improving the efficiency of building high-quality agent applications.
As technology advances, the gap between inspiration, automation, and intelligence continues to shrink. For developers, this represents a deeper form of value liberation than “low-code” ever achieved. It allows humans to delegate repetitive work to machines while retaining imagination and strategic thinking for themselves—re-centering human roles on creativity itself.
