At the top of the image is the title “AI DATA CENTER AGENT PLATFORM”, outlining a system structured around three lifecycle phases and a Data Utilization sector, all orchestrated by the central ‘AI CORE’.
1. DESIGN (Lifecycle Phase 1)
Key Role: Determines optimal infrastructure layouts for high-density GPU clusters, 800V HVDC, and BESS.
Core Technology: Uses CFD and PIML simulations to preemptively eliminate thermal hotspots.
2. CONSTRUCTION (Lifecycle Phase 2)
Key Role: Automates material procurement scheduling and verifies installation compliance for OCP standard equipment.
Core Technology: Streamlines the commissioning process to eliminate human errors and enhance deployment efficiency.
3. OPERATIONS (Lifecycle Phase 3)
Key Role: Executes dynamic power distribution (load balancing) for real-time load fluctuations and optimizes liquid cooling via CDU control.
Core Technology: Performs predictive maintenance with anomaly detection to preemptively prevent failures.
4. DATA UTILIZATION
A. ONTOLOGY (Semantic Knowledge Map)
Defines hierarchical relationships among physical and logical resources (Servers, Racks, PDUs, UPS, Cooling Towers) using semantic modeling.
Utilizes Graph RAG and topology mapping to trace affected VMs or LLM serving Pods within milliseconds during a failure.
Integrates heterogeneous equipment data through standardized schemas (DMTF Redfish, Modbus, BACnet).
B. TELEMETRY (Real-time Streaming Data)
Collects high-frequency time-series streaming data including temperature, wattage, flow rate, and delta-P.
Fuses power infrastructure metrics with IT workload metrics to predict thermal and power peaks proactively.
The table categorizes the key metrics required to safely manage a Coolant Distribution Unit (CDU) and its secondary cooling loop using a 25% Propylene Glycol (PG25) mixture into two main sections:
Real-time Monitoring: This section focuses on physical states that require immediate attention. It includes Temperature (ΔT), Pressure Drop (ΔP), Flow Rate, Leak Detection, and Conductivity.
Example: It highlights that a leak at the rack or joints poses a risk of IT short circuits and fire, dictating immediate actions such as triggering the Emergency Power Off (EPO) and shutting off the loop valves.
Periodic Maintenance: This section covers the chemical and biological fluid quality checks that must be performed regularly. It sets targets for PG Concentration (25% ± 2%), pH Level (7.5 ~ 9.0), Corrosion Inhibitor, Microbes (< 1,000 CFU/ml), and Turbidity/Filtration (< 50 μm).
Example: To mitigate the risk of biofilms clogging the tightly packed microchannels, it advises taking actions like biocide shock dosing or checking the UV sterilization system.
📌 Summary
This document is a practical, structured matrix designed for data center operators. It explicitly outlines the essential operational telemetry, potential hardware and fluid risks, and precise mitigation actions needed to maintain a reliable and highly efficient liquid cooling infrastructure.
This image is a highly structured infographic titled “4 CORE PILLARS OF AI TECHNOLOGY: A CONTINUOUS CYCLE.” It visually explains the architecture of an AI system and its interaction with humans. Arranged around a central AI engine, four primary domains are connected by dynamic arrows, illustrating a continuous, organic flow of information and feedback.
Center – AI Framework & Reasoning: Situated at the very heart of the diagram as a diamond-shaped core, this represents the “brain” of the AI. It acts as the central hub that ingests data from all pillars, processes it, and generates reasoned outputs.
Bottom Left – Numbers (Core Logic & Telemetry): Decorated with mathematical symbols, binary code, and various charts. This section symbolizes the quantitative foundation of AI—the raw data, understanding of change, and the prediction logic that form the physical and structural core of the system.
Bottom Right – Text (Human Knowledge Base): Illustrated with books, documents, and scrolls. It represents the qualitative foundation: human knowledge, context, and language-based insights. This is how the AI learns to understand the nuances and accumulated wisdom of humanity.
Middle Right – UX Output (Insights & Interface): Depicted as a digital dashboard featuring a robot chatbot icon and data summaries. This domain shows how complex AI computations are translated into user-friendly formats. It provides clear “Key Insights,” “Actionable Steps,” and summaries so humans can easily grasp the results.
Top & Middle Left – Human Control & Data Verification: At the absolute top, a human figure in a suit stands as “The Ultimate Authority.” This highlights the human responsibility in rule setting, adding knowledge, and execution confirmation. On the left side, the imagery of hands meticulously analyzing a clipboard and charts—now clearly labeled “DATA VERIFICATION”—emphasizes the critical, hands-on role humans play in evaluating, auditing, and refining the AI’s output.
📌 Summary
This diagram intuitively maps the operational loop of modern AI: raw “Numbers” (logic) and qualitative “Text” (knowledge) feed into the AI engine, which then translates its findings into a user-friendly “UX Output.” Crucially, it highlights that this entire automated cycle is overseen, verified, and refined by humans. It serves as a powerful reminder that “Human-in-the-loop” remains the ultimate authority and the indispensable final step in ensuring AI reliability.
Data & Knowledge Driven AI Agent for Data Center Operations
This illustration describes how data center operations can evolve from facility data → operational knowledge → AI Agent → automated operations.
The key message is not simply that AI controls data center equipment.
Rather, it shows how AI Agents can connect operational data with accumulated human knowledge, understand operational situations, reason about incidents, and support or automate operational actions.
1. Facilities & Systems
The process starts with the physical infrastructure and operational systems of the data center.
The illustration represents:
Power
Cooling
Network
IT / GPU
Security
Environment
Sensors and systems continuously generate operational information.
Systems such as DCIM, NMS, BMS, and log platforms collect this information.
In simple terms:
Facilities generate information, and systems collect it.
2. Data — From Information to Metrics
Facility information is transformed into measurable operational data.
For example:
Temperature → 42.3°C
Power Load → 12.6 MW
Water Flow → 3.2 m³/h
Utilization → 78%
The important point is that the AI Agent uses both:
Real-time Data + Historical Data
Real-time data tells the Agent what is happening now, while historical data provides the operational context and previous experience.
3. Event — Turning Numbers into Meaning
Raw numbers are not always meaningful to operators.
Therefore, data is transformed into understandable events.
For example:
GPU Inlet Temperature is high (42.3°C)
Now the numerical value has become a meaningful operational event.
The event also contains context such as:
What happened + Where + When + Severity + Impact
This is important because the AI Agent does not need to operate only on raw numbers. It can reason about meaningful operational situations.
4. Response — Operational Knowledge
When an event occurs, traditional operations rely on manuals and experienced operators.
The illustration represents this knowledge through:
Runbook
MOP
EOP
SOP
Best Practices
A typical response process can be:
Check → Analyze → Execute → Verify
This represents the transformation of human experience into reusable operational knowledge.
Human Experience → Documentation → Operational Knowledge
This knowledge becomes one of the most important assets for the AI Agent.
5. Final Decision — Judgment & Action
The final stage goes beyond detecting an event.
The operational process becomes:
Root Cause → Action Plan → Service Restore → Record
Traditionally, experienced operators perform much of this reasoning manually.
With an AI Agent, operational data and knowledge can be combined to support:
Root-cause analysis
Action recommendations
Runbook execution
Operator guidance
Controlled automation
In a real data center, however, autonomous action should be governed by policies, safety controls, and human approval where required.
The Center of the Illustration — AI Agent
The AI Agent sits at the center because it connects Data and Knowledge.
Data
Operational Data
Facility & Asset Data
Event & Incident Data
Historical Cases
Asset / Relationship Data
Knowledge
Manuals
Runbooks
SOP / MOP / EOP
Domain Knowledge
Best Practices
Past Cases & Lessons
The Agent combines these two layers to perform:
Learn → Reason → Act
A simple way to express the concept is:
Data tells the Agent what is happening. Knowledge tells the Agent what it means and what to do.
The Core Message
The most important point of the illustration is that the AI Agent itself is not the foundation.
The real foundation is:
Data → Knowledge → AI Agent → Operations
Without accurate data, the Agent cannot reliably understand the current state.
Without structured operational knowledge, the Agent cannot reliably determine what the situation means or what response is appropriate.
Therefore, the real objective of AI-enabled data center operations is not simply:
“Deploy AI.”
It is:
“Make operational data and knowledge usable by AI.”
The Transformation of Data Center Operations
The bottom of the illustration shows:
Data-Driven → Knowledge-Centric → AI-Powered
This represents the evolution of operational models.
Traditional Operations
Human → Data → Manual Analysis → Manual Action
AI Agent-Based Operations
Data + Knowledge → AI Agent → Reasoning → Recommended / Controlled Action
The role of people does not disappear.
Instead, it changes.
AI handles repetitive monitoring, analysis, and operational assistance, while people focus more on judgment, decision-making, exception handling, and continuous improvement.
This is why the final concept is:
People + AI
rather than simply AI replaces People.
One-Sentence Summary
By connecting data generated from data center facilities with operational knowledge, an AI Agent can understand, reason, and support or automate operational actions—transforming human-centered operations into data- and knowledge-driven intelligent operations.
Lechuck Park 