Agents
Enterprise-grade Agents
An enterprise-grade agent built on native Agent Infra,
redefining the paradigm of intelligent, autonomous enterprises
Agentic Ecosystem from Auto Cluster Ops to Fine‑grained Operational Decision‑making
Agentic development tools + large-scale validated "expert agents"
Distributed scheduling engine and secure sandbox for agents
Agent to Agent (A2A) collaboration network
Automated and Intelligent Operations for Complex Large-Scale Clusters
Decision-making for operational resources
Highly Reliable
Build highly reliable and scalable agentic systems
System Autonomy
Move from isolated efficiency gains to system-wide autonomy
Capabilities
Automated analysis of cluster alerts with root cause identification
Closed-loop anomaly detection and automated remediation
Intelligent node provisioning and deprovisioning with self-healing resource states
Supports cross-system log aggregation and causal-chain analysis
Sales-driven automated inventory management and dynamic reconciliation
Load trend forecasting and in-depth resource usage analysis
Data-model-based capacity planning and decision support
Detection of abnormal resource consumption and recommendations for reclaiming idle resources
Distributed scheduling engine, supporting sub-second deployment and elastic scaling
Security sandbox isolation based on containers and virtualization
Layered memory management, supporting structured storage and retrieval of long- and short-term context
End-to-end monitoring and execution tracing capabilities
Message-queue-based, event-driven inter-agent communication mechanism
Supports task distribution, result aggregation, and status synchronization
Built-in workflow orchestration capabilities for automatic execution of complex processes
Standardized interface system compatible with third-party agents, skills, MCPs, tools
Why Us
Use Case
A Large Cloud Computing Service Provider
The customer operates heterogeneous clusters consisting of tens of thousands of servers and faces a massive volume of daily alerts. Manual alert triage resulted in high false-positive rates, and node failure resolution depended on cross-department collaboration, with an average MTTR exceeding 2 hours.
Alert overload: More than 5,000 alerts were generated daily, over 80% of which were invalid or duplicated, making manual triage highly inefficient.
Cumbersome failure handling: Node anomalies required manual login for investigation, workload migration, and node decommissioning, resulting in long, error-prone operational processes.
Intelligent alert noise reduction：Operations agents automatically cluster alerts and perform root cause analysis, suppressing 95% of invalid alerts and reducing MTTR from over 2 hours to 5 minutes.
Intelligent node provisioning and decommissioning: Agents automatically detect node anomalies and independently perform workload migration, reboot, or decommissioning, reducing operations labor costs by approximately 60%.
Improved cluster stability: Failure rates of critical workloads decreased by around 30%, with the impact radius of node-level incidents significantly reduced.
An AI Technology Company
Multiple internal teams shared thousands of GPUs. Resource scheduling relied on manual approvals and experience-based decisions, leading to long queues during peak usage while overall utilization remained low. Diagnosing training failures required coordination across multiple teams, resulting in low operational efficiency.
Slow resource provisioning: Manual approval and allocation led to average wait times exceeding 4 hours.
Low utilization: GPU utilization remained below 65% during peak periods, despite frequent reports of resource shortages.
Delayed inventory visibility: Manual aggregation caused inventory data delays of 1–2 days, preventing real-time operational decisions.
Difficult anomaly diagnosis: Training failures or anomalies required joint troubleshooting by R&D, operations, and platform teams, taking more than 3 hours on average.
Intelligent scheduling：￮Operations management agents analyze resource load and inventory in real time and automatically execute priority-based scheduling and allocation, reducing resource wait times from 3–4 hours to under 30 minutes.
Dynamic coordination：Platform management agents allocate compute based on task urgency and historical usage patterns, increasing GPU utilization from 68% to 82% and reducing resource waste by 20%.
Faster root cause identification：Operations agents automatically aggregate logs and monitoring metrics to generate root cause analysis and remediation recommendations, reducing anomaly diagnosis time from 2–3 hours to under 30 minutes.
Cost optimization：By unifying inventory and workload data, the system enables capacity trend forecasting and proactive resource alerts, achieving an overall annual reduction of approximately 15% in compute costs.
Infinite Computing, Accelerating the Future of AGI
Contact us for customized AI infrastructure solutions
