Data Center Blog #2: From Monitoring to Decision Intelligence: Agentic AI for Data Center Facilities Operations

Across the industrial landscape, AI is driving a new wave of operational efficiency. For data center operators, the very providers of the infrastructure enabling AI, there is a clear opportunity to apply these same capabilities internally. 

As discussed in Part 1 of this series, Seeq Enterprise delivers measurable improvements in critical facilities monitoring and energy management. Seeq Intelligence builds on that foundation, introducing advanced AI capabilities that accelerate decision-making and operational consistency. 

With Seeq Intelligence, domain experts are connected with purpose-built industrial AI, allowing organizations to both guide outcomes and broaden knowledge. Seeq allows data center operators to build analytics that understand their facilities, while Seeq Enterprise enhances options for scaling anomaly detection and triaging events. Seeq Intelligence expands on these capabilities, connecting detected events with institutional knowledge, prior actions, and unstructured contextual data for automating root cause analysis, generating work orders, and other Agentic AI workflows. 

This represents a shift from detection to decision intelligence. 

From Monitoring to Actionable Intelligence

Traditional systems identify anomalies, and lots of them. Operators now need to understand impact, determine next steps, and act quickly and consistently while filtering through the noise of false positives. 

Seeq Intelligence enables this by: 

• Translating complex conditions into clear, plain-language summaries 
• Automatically surfacing relevant procedures, past incidents, and best practices from corporate knowledge stores 
• Recommending next actions based on operational context, historical outcomes, and SME developed analytics  
• Orchestrating workflows across systems through agent-driven automation  

Critically, these insights don’t remain siloed. Through agent-to-agent communication, actions can be executed directly by triggering work orders, informing control systems, or coordinating responses across teams resulting in standardized decision-making, reducing time to resolution, and improving operational efficiency and resilience.  

Amplifying Expertise at Scale

This approach is not about replacing engineers, it’s about amplifying them. 

Seeq Intelligence captures how experts think and operationalizes that knowledge to enable faster, more consistent decisions without losing context. The result is improved performance, reduced risk, and a more scalable operating model. Facilities groups at Intel have already deployed several custom agents within Seeq to automate tasks. Even though they are just getting started with advanced AI-driven workflows, Intel reports that they are saving an estimated 120,000 hours a year of engineering time with a 10-15% ROI through leveraging Agentic AI to enhance how their facilities are monitored. These agents are given autonomy to do things like: 

• Trigger work orders for condition based or predictive maintenance 
• Detect, triage, and perform an initial investigation for anomalies in Chilled/Cooling water systems 
• Augment events that are detected with historical findings from corporate knowledge stores 

AI as a Strategic Advantage

As data centers continue to underpin the growth of AI, applying AI within operations becomes a strategic advantage, not just a technical enhancement. 

In Part 3, we’ll deep dive into how Seeq can be deployed with a focus on energy management within a data center, allowing engineers to build a network of analytics, not only across sites, but between a facility and relevant OEMs. 

To learn more about Seeq, stop by our booth May 27th – 28th at the 6th Annual High-Density AI Data Center Infrastructure Summit in Reston, Virginia.