Trusted by the World’s Leading Companies
What is Agent EyeQ
Agentic AI built for manufacturing operations
Unlike a generic AI platform adapted for the plant floor, Agent EyeQ speaks the vocabulary MES, APS, and SIOP were already built on. It discovers the right capability from a registry of over 1,700 Eyelit operations, and every action it takes is logged, approved, and replayable.
Unlike a generic AI platform adapted for the plant floor, Agent EyeQ speaks the vocabulary MES, APS, and SIOP were already built on. It discovers the right capability from a registry of over 1,700 Eyelit operations, and every action it takes is logged, approved, and replayable.
Reasons
Intent-aware planning
Acts
1,700+ operations
Learns
Knowledge base
Governs
HITL and full audit
Why it qualifies as agentic AI
Beyond chatbots and scripted workflows. Autonomous, tool-using, and grounded in the system of record — Agent EyeQ meets the bar for true agentic AI in manufacturing.
- 01
Reasons and acts in a loop
Decides each next step from live observations. Not a scripted workflow, a reasoning engine that adapts to what it finds.
Read more → - 02
Human-in-the-loop governance
Approval gates, read/write separation, and multi-channel notifications before any change reaches the production system.
Read more → - 03
Discovers tools at runtime
Finds the right capability from a registry of 1,700+ operations by meaning, not hardcoded calls or fixed integrations.
Read more → - 04
Pairs with the no-code engine
Domain experts shape agent behavior with scenarios. No engineering required to extend or constrain how the agent works.
Read more → - 05
Spans all Eyelit Solutions
Reasons across MES, APS, and SIOP in a single agent loop via a common protocol. No system switching, no data gaps.
Read more → - 06
Acts on the system of record
Executes real operations on real lots, not text generation. Real manufacturing outcomes, with full traceability.
Read more →
Checking hold reason, then capacity, then the customer commit
- ✓mes.lot.getStatus(4471)MES
- ✓mes.hold.list(4471)MES
- ✓aps.schedule.simulate(Etch-02)APS
- ✓siop.commit.check(88-102)SIOP
Only quality-gated runs are kept, so the agent gets more consistent over time.
Only quality-gated runs are kept, so the agent gets more consistent over time.
Why it qualifies as agentic AI
Beyond chatbots and scripted workflows. Autonomous, tool-using, and grounded in the system of record — Agent EyeQ meets the bar for true agentic AI in manufacturing.
-
01
Reasons and acts in a loop
Decides each next step from live observations. Not a scripted workflow, a reasoning engine that adapts to what it finds.
Read more → -
02
Human-in-the-loop governance
Approval gates, read/write separation, and multi-channel notifications before any change reaches the production system.
Read more → -
03
Discovers tools at runtime
Finds the right capability from a registry of 1,700+ operations by meaning, not hardcoded calls or fixed integrations.
Read more → -
04
Pairs with the no-code engine
Domain experts shape agent behavior with scenarios. No engineering required to extend or constrain how the agent works.
Read more → -
05
Spans all Eyelit Solutions
Reasons across MES, APS, and SIOP in a single agent loop via a common protocol. No system switching, no data gaps.
Read more → -
06
Acts on the system of record
Executes real operations on real lots, not text generation. Real manufacturing outcomes, with full traceability.
Read more →
Checking hold reason, then capacity, then the customer commit
- ✓ mes.lot.getStatus(4471) MES
- ✓ mes.hold.list(4471) MES
- ✓ aps.schedule.simulate(Etch-02) APS
- ✓ siop.commit.check(88-102) SIOP
Only quality-gated runs are kept, so the agent gets more consistent over time.
Only quality-gated runs are kept, so the agent gets more consistent over time.
What It Can Do
Operating the plant through conversation and autonomy
Conversational platform
Ask in plain language across MES, APS, and SIOP. Get answers and actions, not navigation menus or empty screens.
Autonomous workflows
Multi-step jobs, plan, release, dispatch, hold, schedule, executed end-to-end with human-in-the-loop approval gates.
Live operational insight
Charts, tables, and structured cards built on the fly from real platform data. No dashboard required, no pre-built reports needed.
Faster decisions
Diagnose deviations, suggest dispositions, and draft RCAs and CCAs in seconds using real lot and process data.
Process authoring
Generate flows, scenarios, and task schedules from intent. No XML, no scripting, no scenario UI required.
Full audit and traceability
Every action logged, approved, and replayable, meeting the regulatory and quality requirements of complex manufacturing.
What Agent EyeQ brings to the platform
Built for complex manufacturing, not adapted for it
Delivers seamless interaction across planning, scheduling, and execution
One agentic layer spans domains that used to run separately, and connects to whatever else is already running in your plant.
Starts assisted, grows into autonomous
Every plant begins with a human in the loop and moves toward full autonomy as trust builds, on your own timeline.
Tells you what to do, and why
Agent EyeQ surfaces what happened, why it happened, and what to do next, not just an alert.
Purpose-built for complex manufacturing industries
Not a generic AI platform adapted for the plant floor. Built for it from the ground up.
Supporting detail:
- Model-agnostic architecture, not locked to any single LLM provider
- 1,700+ callable Eyelit operations exposed as MCP tools
- ReAct loop with intent-aware planning
- Quality-gated learning that improves performance over time
- Multi-tenant isolation and permission boundaries
- Human-in-the-loop (HITL) approval workflow with multi-channel notification
- No-code scenario integration for domain experts
- Complete, replayable audit trail
Same system. Different jobs.
The only thing that changes is who’s holding the wheel. Same tools, same governance, deployed to match the task at hand.
Helps you do your job better
Sits next to the operator. Suggests, drafts, recommends, and executes on request. The user stays in charge at every step.
- Drafts dispositions, RCAs, and schedule changes for review
- Surfaces relevant operational data on demand — no dashboards to build
- Walks junior engineers through unfamiliar tasks
- Accelerates routine work for experienced operators
Who’s holding the wheel
You act on the agent’s suggestions. It drafts, you decide.
Real Insights and Real Results
Case studies and practical outcomes from complex manufacturing operations.
Case Study8 steps to improve plant performance
According to the Office of National Statistics (ONS), UK workers produced less during 2014 than they did in 2007. The actual output per worker fell by 0.2 per…
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Case Study6 Essential KPIs for Manufacturing Performance
Depending on the systems and processes you have in place on your factory floor, you may face one of two problems; either you don’t know which key performance…
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Case StudyWhy Are You Still Using Spreadsheets for Production Planning & Scheduling?
Are you still spending time on spreadsheets adjusting production plans and schedules? If so, you are not alone.
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Case StudyHow to turn small batch manufacturing from problem to opportunity
James Womack, author of the best-selling business classic text ‘Lean Thinking’, tells the story of how he challenged his children to an envelope-stuffing competition.
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Case StudyIndustry 4.0: don’t get left behind
Industry 4.0, smart manufacturing, big data, IoT, advanced manufacturing intelligence… you’ve heard all the buzzwords, but what do they really mean, and what’s their relevance to your manufacturing…
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Case StudyRevenue per full-time employee – why it’s a waste of your time…
Revenue per full-time employee – why it’s a waste of your time… The chances are that the way you measure the productivity of your workforce is to calculate…
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Case StudyFive must-have features for your next shop floor data collection system
Investing in any new system for your business can have far-reaching consequences. Apart from the direct costs involved, you will be conscious of the time and resources…
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Case StudyPlanning and Scheduling – A Key Enabler of Industry 4.0 Transformation
Industry 4.0 envisages a flexible network of connected Cyber Physical Systems (CPS), working autonomously and self-optimizing performance across the network.
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Case StudyThe Digital Twin’s Role in the Production Environment
A digital twin is a digital representation of a real-world entity that contains both the structure and the dynamics of the real-world entity. It is connected to its…
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Case StudyIndustry 4.0 Implementation and Strategy
Today most production managers are participating in a race, whether they know it or not. It's the race to adopt and implement new manufacturing systems and technologies.
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Case StudyMulti-Stage Scheduling
In the automotive industry, the entire supply chain is driven by the assembly line sequence. Links within the chain are relied upon to produce and deliver parts on…
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Case StudyHow to Prepare for Supply Chain Automation & Stay Competitive
Market forces and technological advancements continue to accelerate the already unprecedented levels of information and product exchange around the world.
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Case StudySupercharging the Digital Twin with Planning & Scheduling 4.0
The digital twin is a powerful Industry 4.0 capability that is transforming manufacturing operations.
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Case StudyMulti-Zone Sequencing
Eyelit Technologies MLS, versions 7.0 and beyond introduce multi-zone production sequencing, a new approach to optimization which can benefit plants with the need to generate a sequence for…
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Case StudyHow Can Manufacturers Become More Sustainable
The Automotive Industry Action Group (AIAG) is hosting a Virtual Corporate Responsibility Summit on April 28. Leading automotive manufacturers will come together to discuss production planning and scheduling,…
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Case StudyStabilize Your Supply Chain
Optessa Founder and CEO Ashok Erramilli discusses the power of data-driven analytics and the importance of achieving balance and stability in production management.
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Case Study5 COMPONENTS OF PRODUCTION SCHEDULING
Production Scheduling is "used in a manufacturing process to allocate plant and machinery resources, plan human resources, plan production processes and purchase materials". What are the components of…
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Case StudyEyelit’s Integrated Manufacturing System (MES) Selected and Deployed by WD Lab Grown Diamonds
TORONTO—(BUSINESS WIRE)—Eyelit, a manufacturing software provider for visibility, control, and coordination of manufacturing operations, announced today that WD Lab Grown Diamonds
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Case StudyEnterprise Production Scheduling Solutions
Enterprise manufacturers need manufacturing scheduling software that can simultaneously simplify the complexity of the detailed scheduling process, boost the efficiency of the production process, and ensure on-time production…
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Case StudyModern CTO Podcast | Episode #284 | Erwin Paes
Erwin Paes spoke with Modern CTO Podcast host Joel Beasley on the intricacies of planning and scheduling in manufacturing, how Optessa's greatest strength is their people, and the…
Read more →Ready to meet your next coworker?
Avoid another year of decisions made in silos, alerts with no explanation, and your best people buried in dashboards instead of strategic work. See Agent EyeQ reason, act, and deliver outcomes across your manufacturing platform, in a live demo tailored to your environment.
