Agentic AI
Agent EyeQ
Goal-driven AI engine that reasons, acts, and learns — executing real work across MES, APS, and SIOP with auditable governance.
Request DemoWhat is Agent EyeQ
Agentic AI built for manufacturing operations
Agent EyeQ brings autonomous reasoning, action, and learning to the Eyelit platform. Unlike conventional AI tools that generate text or run scripted workflows, Agent EyeQ operates inside the manufacturing system of record — planning, releasing, dispatching, and scheduling with the same vocabulary MES, APS, and SIOP were built on.
It spans the full platform in a single agent loop, discovers the right capability from a registry of over 1,700 Eyelit operations, and executes with complete auditability — every action logged, approved, and replayable.
The result: a coworker that helps operators move faster, and an autonomous employee that can own jobs outright — 24/7, against assigned KPIs and constraints.
Reasons
Intent-aware planning
ReAct loop decides each next step from live observations — not a scripted workflow.
Acts
1,700+ operations
Full Eyelit platform as callable tools — MES, APS, and SIOP in one unified loop.
Learns
Knowledge base
Every quality-gated run feeds a vector store the agent draws on in future sessions.
Governs
HITL + full audit
Approval gates, read/write separation, multi-channel notifications before any change.
Two Modes
Caddy of Employee
Caddy Mode
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
Employee Mode
Owns the job outright
Given a goal, not a script. Plans, executes, and escalates by exception. Reports to a human manager.
Runs 24/7 against assigned KPIs and operational constraints
Acts autonomously within human-in-the-loop guardrails
Learns from every run via quality-gated transcripts
Full audit trail — every action traceable and replayable
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.
Inside Eyelit
Compressing delivery, support, and engineering cycles
Agent EyeQ also works inside Eyelit — accelerating how we build, support, and onboard across the platform.
JIRA Triage and Bitbucket Dig
Reads tickets, locates relevant code, proposes fixes, and drafts patches from a unified knowledge base of the codebase.
Source-aware diagnostics
Traces issues from Angular through to backend events using a deep code knowledge base — from symptom to root cause.
Faster onboarding
New engineers learn the platform by talking to it — backed by a curated, quality-gated knowledge base of the platform.
In Progress
Headless QA Loop
Set scientifically calculated safety stock targets at every supply chain tier — raw material, WIP, sub-assembly, finished goods, and distributor — using multi-echelon inventory optimization (MEIO) to minimize total pipeline cost while meeting service level objectives.
Customer support copilot
Answers product questions grounded in real customer configuration and live system state — not generic documentation.
In Progress
Scenario authoring
Authors Eyelit scenarios directly from user instructions — without anyone touching the scenario UI.
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.
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.
Human-in-the-loop governance
Approval gates, read/write separation, and multi-channel notifications before any change reaches the production system.
Discovers tools at runtime
Finds the right capability from a registry of 1,700+ operations by meaning — not hardcoded calls or fixed integrations.
Pairs with the no-code engine
Domain experts shape agent behavior with scenarios — no engineering required to extend or constrain how the agent works.
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.
Acts on the system of record
Executes real operations on real lots — not text generation. Real manufacturing outcomes, with full traceability.
Key Capabilities
What Agent EyeQ brings to the platform
Built for complex manufacturing environments with the governance, depth, and integration that production operations require.
Model agnostic architecture: Not locked to any single LLM provider. The reasoning layer is swappable as the model landscape evolves.
1,700+ callable Eyelit operations: MES, APS, and SIOP operations exposed as MCP tools — the full platform available to the agent at runtime.
ReAct loop with intent-aware planning: Observe, reason, act, repeat — adapting to live results rather than executing a predetermined script.
Quality-gated learning: Approved agent runs feed a vector knowledge base that improves performance and consistency over time.
Multi-tenant tenancy: Isolation and permission boundaries that meet the IP and data requirements of multi-customer environments.
Humain-in-the-loop (HITL) approval workflow: Configurable human checkpoints before consequential actions — with multi-channel notification and full logging.
No-code scenario integration: Domain experts define agent constraints and behaviors using Eyelit’s scenario engine — no engineering required.
Complete audit trail: Every action, approval, and outcome is logged and replayable — meeting regulatory and quality requirements.
Resources
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Article
Why General-Purpose LLMs Fall Short in Complex Manufacturing
There is a lot of discussion right now about the role of large language models (LLMs) in manufacturing. The excitement…
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Spotlight
Six Key Requirements for Effective Planning and Scheduling
Optimizing a modern-day manufacturing facility is a complex task. Manufacturers face an overwhelming array of conflicting demands, including reducing stock,…
Read more
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Video
Watch: The Evolution of APS — Advanced Planning & Scheduling Systems
As supply chains continue to grow ever more complex, advanced planning and scheduling systems have evolved to enable their optimization,…
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Press Release
Eyelit Technologies Recognized in the 2026 Gartner® Magic Quadrant™ for Supply Chain Planning Solutions: Discrete Industries
Leading provider of industry-specific planning and scheduling solutions earns recognition for innovation in autonomous planning and execution-aligned decision-making HOLMDEL, N.J.…
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