AI gives your team real-time process optimization by connecting data across your IT/OT data sources (e.g., historian, MES, ERP, and CMMS systems) as data is generated. An AI agent removes the friction of navigating siloed systems for improvement opportunities, freeing process engineers and production teams to focus on removing efficiency bottlenecks, maximizing capacity, and protecting yield and throughput.
Plant floors are more tech-enabled than ever before. The problem is that highly skilled production and process Subject Matter Experts (SMEs) are still stuck being the “glue” between the different data systems running within your operations. Manually combining data from your historian, MES, ERP, and CMMS systems is time-consuming, ineffective, and a waste of your team’s talent.
This means that, even though operational data is plentiful, the insights teams need to improve yield, quality, and throughput often come in too late to maintain continuous production (if at all).
The Industrial AI Workforce is emerging as a critical answer to solving these manufacturing operational challenges. AI agents assist your teams by connecting data from different OT/IT sources, and recommending the correct next steps as soon as events or scenarios arise. Eachagent is specialized to address specific plant roles and learns how to improve with every run.
Real-time process optimization agents are one example of a domain-specific agent for industrial manufacturing. This article explains what a process optimization agent is and the various industrial data sources it integrates to deliver rapid process recommendations. It also covers three key benefits of using an operations agent within your manufacturing facility: flagging live process drifts, anomalies, and yield deviations; automated root cause analysis; and evidence-based recommendations for yield and quality improvements.
What an AI agent does for real-time process optimization
Instead of replacing your process engineers, an AI agent removes the manual work of connecting disconnected systems so your team can focus on solving problems. Augury’s Operations Agent handles that connection automatically, in real time.
Operations teams are perpetually weighed down by navigating siloed IT/OT systems and disconnected data sources. It takes an exorbitant amount of time to bring data from isolated systems (e.g., your MES, SCADA, historian, ERP, and LIMS) into a consolidated place for complete production visibility.
By streamlining access to all your systems, a process optimization AI agent can:
- Reduce scrap rates or prevent off-spec yield by detecting deviations and drifts long before they trigger alert thresholds.
- Keep output on target by surfacing real-time setpoint recommendations for quality and yield optimizations.
- Minimize production line downtime by delivering automated, evidence-based RCA in minutes after analyzing historical data alongside real-time process parameters.
- Standardize early anomaly detection for rate loss and process drift playbooks by capturing your team’s operational knowledge, SOPs, and documentation into a Standardized Knowledge Base (SKB) that acts as the AI’s source of truth for dynamic task execution.
Best of all, using an agent to connect system data keeps your team focused on the most important part of their job: making sure your operations run as efficiently, profitably, and safely as possible.
For example, a large industrial mining manufacturer used the Operations Agent from Augury to predict and prevent dryer plugging before it stopped production. This was possible by connecting operational and machine-health signals, identifying the root cause, and guiding operators to the right corrective action in time to protect throughput.
Connecting IT and OT manufacturing data in real-time with an AI agent
Instead of manually moving your data to the agent, the agent ingests data from where it already lives so it can run analyses on live data rather than simulations. A process optimization agent connects directly to your historian, MES, ERP, SCADA, and CMMS systems without requiring a new data platform or migration, making it possible to convert fragmented data signals into a single, real-time operational picture.
Simulation vs. AI agent: two approaches to process optimization
| Digital twin/simulation approach | Live-data agent approach | |
| Foundation | A modeled representation of your process, built and validated over time | Live and historical data comes in directly from the systems running within the plant |
| Getting started | Requires building and validating a process model | The agent comes to where your data already lives: no new platform, no data migration |
| Best suited for | Testing “what-if” scenarios before they take place on the floor | Catching, acting upon, and improving what’s happening on the floor in real time |
| Data currency | As current as the last model update | Updates continuously as new data arrives |
Advanced process optimization agents, such as Augury’s Operations Agent, draw directly from your integrated data sources. Augury’s Industrial AI Workforce connects the system and data sources within your plant, from your hardest-to-reach assets to your most vital equipment, ensuring that each agent operates from the same centralized cognitive layer.
By combining sensor data, lab results, batch records, documentation, SOPs, and tribal knowledge into one system, you end up with an AI specific to your site. This is a critical and valuable way to overcome data limitations, which 8 in 10 companies cite as their biggest roadblock to scaling agentic AI, according to McKinsey.
It’s also what enables a real-time process optimization agent to surface bottlenecks and hidden losses as they emerge, before you pull a shift-end report.
For example, a specialized agent can correlate a historian tag with a quality-system reading without requiring an engineer or data scientist to export and reconcile multiple datasets. The agent delivers recommended setpoints and a prioritized view of where yield and throughput are most at risk. Your team gets both the contextual information for the agent’s findings and evidence-based setpoint recommendations so they can make the right call before taking action.
How an AI agent catches process drift and yield deviations as they happen, before defects arise
With continuous access to live data from production, a process optimization agent flags rate loss, yield deviation, and process drift before you miss a production target, produce an off-spec run, or create a negative downstream defect.
AI agents are a major improvement over process dashboards because no one has to read a chart or wait for an alert to pop up to know something is wrong: the agent automatically recommends the right set point adjustments and surfaces hidden losses, leaving the final decisions about what to do in the hands of your team. A chat interface allows customers to ask questions, view reasoning, and understand what to do next.
Detecting process drift early is an unprecedented benefit of using agents to improve process optimization. Right now, most manufacturers rely on alerts that are triggered when a static threshold is reached. But a gradual drift in pressure or temperature won’t trip your alert system until the threshold is met, increasing scrap, downtime, and rework.
An agent detects the trend and notifies teams what’s happening before conditions hit an alert-level status. With an agent monitoring production around the clock, your engineers can act quickly to maintain stable yield and continuous production.
How a process optimization agent points teams toward root causes and yield opportunities
Augury is extending its process optimization agent’s reasoning layer to correlate process variables, historical patterns, and site knowledge against likely root causes and yield opportunities. As a result, the AI agent can explain why a deviation occurred (instead of just flagging it) and point your team toward the right steps for resolution.
This reasoning layer enables the agent to help plant teams with:
- Root cause: The AI correlates process parameter shifts (like temperature, speed, or pressure drift) against your site’s batch history and SOPs. This helps the agent surface the conditions most likely driving a given deviation, giving your team a strong head start on RCA from a short list of likely culprits.
- Yield recommendations: By associating asset health signals with yield data, an agent can flag where a specific piece of equipment is negatively impacting output before it shows up in a scrap or off-spec report.
- Bottleneck reduction: Connecting reliability data with line-level throughput data enables the AI to pinpoint where a machine health issue is causing a throughput bottleneck ahead of missing a production target.
- Stoppage prevention: By connecting machine health signals with production data, an agent can identify patterns that often precede an unplanned stoppage and surface the issue early enough for the team to take action.
All of these agent capabilities use the same reasoning layer. As AI models advance exponentially, agents can reason faster, better, and more accurately across a wider variety of data sources.
Why an AI agent augments your process engineers, not replaces them
An AI agent takes over the repetitive work of gathering and reconciling data across disconnected systems, so process engineers spend less time chasing information and more time on the diagnosis and judgment calls only they can make from their expertise.
Think of a process optimization agent as an expert advisor on real-time plant conditions. For example, Augury’s Operations Agent recommends exact setpoint adjustments and surfaces hidden losses for your engineers, but your team stays in total control of the final actions through a human-in-the-loop architecture.
The goal of integrating an Industrial AI Workforce into your plant operations should not be to capture your team’s expertise only to replace them. Instead, the role of AI within manufacturing operations should be to:
- Make intrinsic and tribal team knowledge more widely accessible across multiple teams, lines, or facilities
- Free your highly-trained engineers for work that makes the most of their valuable skill set, learned experience, and problem-solving abilities.
- Protect institutional knowledge as experienced engineers retire, to help incoming employees learn the ropes quickly and more easily
This is one reason why Augury assigns a dedicated Forward Deployed Engineer (FDE) team to work directly with your plant to build out the DNA of your operations. This team includes a Process SME, Data Engineer, and Gen AI/ML Engineer, all of whom work with you to configure the agent to your plant’s specific systems, data sources, and workflows. By working alongside your experts, you can adopt agents trained on your specific SOPs and goals in just 12 weeks.
For Avraham Boublil, R&D Director at ICL Iberpotash, using a process optimization agent completely changed what his team could accomplish. Boublil and his team use Augury’s Operations Agent to ensure consistent potash production across four shifts at a plant running around the clock.
Once you show a guy he can do an RCA in a minute, this is priceless,” said Boublil. “If I have a question, I ask the tool, and that’s it…We are basically making the gap disappear.”
The compounding value of a process optimization agent: better throughput, yield, and capacity over time
A process optimization agent supports continuous improvement by capturing every validated fix in a Standardized Knowledge Base (SKB): an operational playbook that captures this tribal and data-backed knowledge in one place and can be used at every site. This snowballing value also compounds across plants, not just within a single plant: a gain on one line becomes a starting point for the next run, at any plant, instead of just a one-off win.
That means that the next time a similar deviation shows up on a different line or at a different plant, the agent can present a human-validated path to resolution instead of forcing your team to solve the problem from scratch. Your team can continuously improve without requiring humans to document, standardize, and roll out every fix manually.
After all, this is where manufacturing process optimization is headed. Periodic reviews and end-of-shift reports will give way to autonomous systems that connect your operational data sources, monitor and notify teams about changes, and recommend evidence-based next steps. Best of all, the decision-making stays in your team’s hands, supported by an agentic system that gets smarter with every run.
FAQs
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How can AI enable real-time process optimization?
AI enables real-time process optimization by connecting plant data into one place for constant observation so it can bring drifts, deviations, or issues to your team right away instead of in shift reports or relying on manual review. Augury’s Operations Agent works this way, drawing directly from the systems already running on your plant floor to help process engineers catch drift and yield issues while there’s still time to intervene, not after the fact.
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What is an AI agent for process optimization?
An AI agent for process optimization automatically connects your plant’s IT/OT operational systems into a single real-time view and monitors your production data continuously for deviations. The agent can surface process drift and yield issues as they occur, flag bottlenecks before they degrade production, and provide setpoint recommendations to process engineers for rapid resolutions.
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How is an AI agent different from using a process optimization dashboard?
An operations dashboard can show your team what’s happened but relies on someone to actually notice what’s happened, either by seeing it on a chart or getting an alert, before they can investigate the cause and decide what to do next. By comparison, an AI agent works continuously in the background, flagging deviations or drifts as they occur. With an agent monitoring production around the clock, process engineers can get clear answers about what’s happening (and how to fix it) before you miss your production target.
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Can operations teams reduce throughput bottlenecks in real time using AI?
Most throughput bottlenecks start as a machine health issue that slows down production before triggering an alert. Correlating reliability data with line-level throughput data enables an AI agent to flag drifts or slowdowns as they’re happening so teams can quickly resolve the issue. Augury’s Operations Agent, for example, connects reliability data with throughput data in this way, enabling it to pinpoint where a machine health issue is likely to cause a throughput bottleneck before yield targets are missed.
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Can AI agents recommend actions to improve production yield?
Yes, agents can continuously correlate asset health signals with production and yield data, making it possible to flag where a specific piece of equipment or process condition is jeopardizing output. For instance, Augury’s Operations Agent uses an advanced reasoning layer to associate machine health signals with yield data, surfacing risks automatically and in real-time. This gives teams time to adjust setpoints or correct issues before you have to scrap a run or they show up in an off-spec report.
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Does an AI process agent require a new data platform?
No. Augury’s Operations Agent connects directly to the systems already running in your plant (including your historian, MES, ERP, LIMS, and CMMS) without requiring a new data lake or migration project. The agent is deployed to where operational data already lives, so IT and operations teams avoid a separate infrastructure buildout before seeing value.
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Does an AI process agent replace process engineers?
Agents should augment process engineers by taking over the repetitive grunt work of gathering and reconciling data across disconnected systems, not replace them. Agents can also provide evidence-based setpoint recommendations or RCA, which keeps your engineers from starting at point zero every time an issue arises. Most importantly, engineers maintain control of every decision, including those recommended by agents.
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How do AI agents help identify the root cause of a process issue?
Advanced agents, like Augury’s Operations Agent, use a reasoning layer that correlates your process variables, historical patterns, and site knowledge against known root causes. The agent can do more than just flag deviations or downstream issues: it can explain what likely happened and how teams can course-correct before the problem becomes unmanageable.
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How can AI agents help with continuous process improvement in industrial manufacturing?
AI solutions, like Augury’s Operations Agent, capture every human-validated fix in a Standardized Knowledge Base (SKB), so knowledge gained on one line becomes a starting point for the next run, on that line or another comparable line. This is a big step forward compared to traditional continuous improvement efforts, which usually depend on someone manually documenting a fix and then sharing it across lines or plants. Agents help your team continuously improve production, both in the plant where an issue occurred and at other facilities, without requiring human teams to manually document and standardize every fix.
