From Insight to Action: How Seeq’s Agentic AI Is Transforming Semiconductor Facilities Operations

In a semiconductor fab, the difficult part is not detecting an anomaly, process engineers get thousands of anomaly email alerts each day. It is determining whether the anomaly matters, how serious it is, how it was handled if it has been seen before, and what should happen next. 

That investigation typically crosses several systems. An engineer needs to look for process signals in a historian or large data store, review maintenance history, look for an FMEA or troubleshooting procedure, and contact a tech to look at the equipment. The work is highly technical, but much of the effort is spent assembling data and context across sources and repeating steps that may already have been performed at another site or on another shift. 

This is the operational gap that Seeq’s agentic AI addresses. The goal is not to place a general-purpose chatbot in front of a user, but rather to connect subject-matter-expert analytics with the information and execution systems required to move from an abnormal condition to a validated response and action. 

The real problem is not a lack of data

Facilities and sub-fab operations generate more data than most teams can review manually. When a threshold is breached, an alarm may be the first indicator that something is awry, or alternatively it could simply be a nuisance due to a mis calibrated limit. It is up to the engineer to explain whether that first signal is caused by a process transition, a sensor issue, a component failure, or a known maintenance condition, a limit that needs to be changed, or an issue that has already been resolved elsewhere. 

At Intel, the team describes this separation between control, telemetry, analytics, reporting, and asset management as a source of fragmented visibility and reactive work. To address these problems, Intel did not simply add another dashboard, rather they sought to create a shared operational context space in which signals, assets, events, procedures, and prior decisions can be interpreted together with the help of Agent Q, Seeq’s AI orchestration layer. Learn more about Intel’s use case here. 

What changes when an anomaly becomes a workflow 

Consider a condition monitor that identifies abnormal behavior in an ultrapure water system. In a traditional workflow, the event may produce an alarm or notification and wait for an engineer to begin the investigation. In an agentic workflow, the event becomes the starting point for a sequence of SME defined steps. 

First, the Anomaly Detection Agent records the event with the information needed to investigate it: the primary SCADA tag, contributing tags, site, discipline, equipment type, and other relevant metadata. The event is logged in a Seeq Vantage Room, where it can be tracked and reviewed alongside other facility conditions. 

Seeq’s Agent Q then interprets that event. It can assemble the relevant time-series signals, retrieve asset details and potentially related maintenance events, and identify historical context. Instead of asking an engineer to reconstruct the investigation from a blank worksheet, the system prepares a starting point based on the event itself. 

The next step is to document and procedural context. A Knowledge Agent queries a company knowledge repository for applicable troubleshooting specifications, standard operating procedures, maintenance instructions, and related documents. The response can include recommended actions, precautions, related procedures, and a reference to the original source document. That last element is important: the engineer can verify the recommendation rather than treating the agent as an authority or black box. 

If the evidence indicates that maintenance is required, the Asset Management Agent can retrieve work-order and asset information and, in cases where an engineer has deemed appropriate, create a new work order with the relevant context. The user remains responsible for validation when the workflow requires it. 

The result is not simply a faster alert. It is an automated triage: the event, the contributing evidence, the relevant procedure, the analysis context, and the proposed next action are brought together in one workflow and presented to an engineer for validation. 

Learn how Intel worked with Seeq to create this multi-agent architecture here. 

The engineering constraint: autonomy must be bounded

Manufacturing operations are not a suitable environment for vague claims of unrestricted autonomy. A useful system must be explicit about what it can do, what it cannot do, and where a person must approve the result. 

The agents described in Intel’s Conneqt 2026 presentation, referenced in the image above, can retrieve information, provide guidance, create analyses, and generate work orders based on defined conditions or user requests. They are not designed to modify or delete existing work orders, directly control equipment or safety systems, change system permissions, or replace qualified engineering judgment. 

Intel describes this approach as “dimming the lights, not the intelligence.” Routine investigation and administrative work can be accelerated, while consequential actions remain subject to governance. Intel’s broader automation-to-autonomy architecture uses human-in-the-loop checkpoints for higher-risk actions and records model metadata and confidence information for traceability.

This boundary also defines the role of a language model. It can be effective at searching documents, summarizing evidence, and communicating through natural language. It should not replace SME–led analytics and root cause investigations, statistical methods, control logic, or process knowledge that determine whether an operational conclusion is valid. SME-governed analytics instead combine with and are leveraged by AI agents to retrieve context and coordinate SME–curated workflows. 

Realizing measurable value

Intel has reported measurable results from applying this model to facilities operations: 

• 30% faster anomaly detection in UPW systems, associated with approximately $7.5 million in annual savings from preventing excursions. 
• A 15–20% reduction in technician workload through autonomous work-order generation. 
• More than 120,000 labor hours are saved annually by integrating asset-management data with industrial process analytics. 
• A reported 15% increase in chilled-water plant energy efficiency. 
• A target of 10–15% ROI, thousands of hours saved per year, three integrated agents, and continuous monitoring as the solution scales. 

These outcomes do not come from a single AI feature or chatbot/LLM question. They come from connecting SME-curated analytics, condition monitoring, predictive models, asset context, procedures, and past event context to drive controlled execution of AI workflows delivering faster time to response and increased uptime.

This approach can be adapted for use cases like compressor maintenance, UPW monitoring, hybrid statistical and machine-learning anomaly detection, and digital-twin-supported chilled-water optimization. In each case, the objective is to replace fixed, time-based responses with decisions based on equipment condition, predicted behavior, business constraints, and maintenance resources. 

Driving towards human-on-the-loop

The semiconductor industry does not need another disconnected dashboard. It needs a way to connect operational data with the procedures, engineering judgment, prior event context, and execution systems that determine what happens next. 

Intel’s Conneqt 2026 presentation shows practical progression from time-based and alarm-driven maintenance toward condition-based, predictive, and agent-assisted operations. The near-term value is concrete: earlier detection, fewer manual handoffs and data pulls, better use of maintenance resources, improved energy performance, and more consistent decisions across sites. 

For manufacturers under pressure to increase uptime and capacity, or simply lower costs, without waiting years for new facilities, hidden capacity and cost optimization is unlocked by uniting the data, context, and expertise that have historically been difficult to connect. Agentic AI makes that knowledge available at the moment of decision, turning insight into action while keeping people in control. 

Request your demo to see how Seeq can complement and enhance your semiconductor manufacturing technology.