Agentic AI in Manufacturing

Level: Intermediate

Table of Contents

1. Introduction — LLMs and AI Agents
2. Predictive Maintenance
   • 2.1 Sensor Data and Repair Logs
   • 2.2 Maintenance AI Agent Architecture
   • 2.3 Multi-Agent System for Maintenance
3. Improving Production Planning
   • 3.1 Scheduling Factors
   • 3.2 Production Scheduling Agent
   • 3.3 Multi-Agent Collaboration
4. Real-Time Defect Detection
   • 4.1 AI Vision Agents
   • 4.2 Training Vision Models
   • 4.3 AI Listening Agents
   • 4.4 Complete Real-Time Detection Architecture
5. Continuous Quality Improvement
6. AI Costs vs. Production Cost Reduction
   • 6.1 Implementation Costs
   • 6.2 AI Hallucinations
   • 6.3 Cost-Benefit Analysis
   • 6.4 Recommended Deployment Strategy
7. Summary — Complete Multi-Agent System

1. Introduction — LLMs and AI Agents

Large Language Models (LLMs) are machine learning models trained on massive datasets. They can communicate and reason similarly to humans through neural networks analogous to those in the human brain — which is why the vocabulary borrows from neurology.

          ┌────────────┐
Prompt ──►│    LLM     │──► Output
          └────────────┘

To be useful in industrial manufacturing, LLMs must take on the role of AI agents. An AI agent differs from a plain LLM by its capacity to:

• Perceive its environment (input data)
• Reason (via the LLM)
• Act autonomously (tools, APIs, other agents)

AI Agent Architecture

graph TD
    subgraph INPUTS[Inputs]
        TLM["• Telemetry\n• Recent events\n• Record IDs\n• HTTP calls\n• Messages"]
    end

    subgraph MEMORY[Memory]
        STM["Short-term Memory\n────────────────\n• Dynamic goals\n• Current constraints\n• Current context\n• Variable targets"]
        LTM["Long-term Memory\n────────────────\n• Machine manuals\n• Historical data\n• Factory topology\n• Data models"]
    end

    subgraph CORE[Core]
        PG[Prompt Generator API]
        LLM["LLM\nCore Brain"]
    end

    subgraph OUTPUTS[Outputs]
        OI[Output Interface API]
        SW[Software Tools]
        COM[Communication Tools]
        AA[Other AI Agents]
        HW[Hardware]
    end

    INPUTS --> PG
    PG --> LLM
    STM --> LLM
    LTM --> LLM
    LLM --> OI
    OI --> SW
    OI --> COM
    OI --> AA
    OI --> HW

2. Predictive Maintenance

2.1 Sensor Data and Repair Logs

Production lines continuously capture real-time sensor data:

╔══════════════════════════════════════════════════╗
║         Industrial Sensor Data                   ║
╠══════════════════════════════════════════════════╣
║  Temperature       Power usage                   ║
║  Pressure          Rotation speed                ║
║  Speed             Acoustic signals              ║
║  Vibrations                                      ║
╚══════════════════════════════════════════════════╝

This data is combined with repair logs (maintenance records) to form the training foundation for the predictive model.

Traditional approach:

Sensor Data ──► Central database ──► Human (manual review)
                                 └──► Rule-based system (threshold alerts)

Problem: Alerts triggered too late result in costly production outages.

2.2 Maintenance AI Agent Architecture

flowchart TD
    SD["Factory Sensor Data\n(Speed, Pressure, Vibrations...)"]
    RL["Repair Logs\n(Maintenance history)"]
    DB[("Central\nDatabase")]
    ML["ML Training\nModel training"]
    PM["Predictive Model\nDetects upcoming\nfailure patterns"]

    SD --> DB
    RL --> DB
    DB --> ML
    ML --> PM

    subgraph MSA["Maintenance Scheduling Agent"]
        direction TB
        LLM2["LLM\nReasoning & Language"]
        PM2["Predictive Model\nProjection accuracy"]
        STM2["Short-term Memory\n• Factory goals\n• Current schedule\n• Production targets"]
        LTM2["Long-term Memory\n• Machine manuals\n• Factory topology\n• Factory schedule"]
        LLM2 <--> PM2
        STM2 --> LLM2
        LTM2 --> LLM2
    end

    PM --> MSA
    MSA --> SCHED["Prioritized\nMaintenance Schedule"]

The predictive model:

• Trains on historical data (sensor data + repair logs)
• Identifies subtle patterns signaling an imminent failure
• Delivers accurate projections on maintenance needs
• Enables action before a breakdown, not after

2.3 Multi-Agent System for Maintenance

graph TD
    subgraph MSA["Maintenance Scheduling Agent\n(LLM + Predictive Model)"]
        STM_M["Short-term Memory\n• Production targets\n• Factory schedule"]
        LTM_M["Long-term Memory\n• Machine manuals\n• Factory topology"]
        PG_M[Prompt Generator]
        OI_M[Output Interface]
    end

    subgraph RA["Reporting Agent\n(Large Language Model)"]
        STM_R["Short-term Memory\n• Shift schedule\n• Report templates"]
        LTM_R["Long-term Memory\n• Report templates"]
        PG_R[Prompt Generator]
        OI_R[Output Interface]
    end

    SCHED["Maintenance Schedule\n────────────────────────\n1. Prod-line 3 : Component D\n2. Prod-line 1 : Component S\n3. Prod-line 2 : Component A\n4. Prod-line 3 : Component C"]

    MSA --> SCHED
    SCHED --> RA
    OI_M -->|"Report Generation Tool"| OI_R
    OI_R -->|"Email Server"| RPT["Daily report\nemailed to managers"]

Advantages vs traditional approach:

3. Improving Production Planning

3.1 Scheduling Factors

Production scheduling involves planning what, when, and how to produce to meet objectives — while remaining efficient, minimizing downtime, and adapting to changes.

╔═══════════════════════════════════════════════════════════════════╗
║             Production Scheduling — Factors to Consider          ║
╠═══════════════════════════════════════════════════════════════════╣
║  CORE QUESTIONS                  OPERATIONAL CONSTRAINTS         ║
║  ──────────────────              ───────────────────────────      ║
║  • What to produce?              • Factory condition              ║
║  • When to produce?              • Active production lines        ║
║  • Which resources?              • Equipment failures             ║
║  • How to optimize?              • Shift patterns                 ║
║  • Minimize downtime?            • Machine status & capabilities  ║
║  • Meet deadlines?               • Inventory levels               ║
║  • Adapt to changes?             • Holding costs                  ║
╠═══════════════════════════════════════════════════════════════════╣
║  INPUT DATA                      PERFORMANCE INDICATORS (KPIs)   ║
║  ────────────────────            ────────────────────────────     ║
║  • Quarter targets               • On-time delivery rate          ║
║  • Forecast data                 • Resource allocation            ║
║  • Production velocity           • Production efficiency ratio    ║
║  • Delivery dates                • Production line utilization    ║
║  • Production batches            • Setup costs vs. holding costs  ║
║  • Supply chain planning         • Optimization savings           ║
║  • ERP systems data              • Delivery quantity              ║
║  • Demand variance               • Variable options               ║
╚═══════════════════════════════════════════════════════════════════╝

Traditionally, all this complexity is handled by human teams through:

• Parameters and algorithms
• Spreadsheets
• Rule-based applications
• Historical data

This process is time-consuming, repetitive, expensive, and prone to human error.

3.2 Production Scheduling Agent

flowchart LR
    subgraph OLD["Traditional Approach"]
        PARAMS_H["• Parameters\n• Algorithms\n• Spreadsheets\n• Rules-based apps\n• Historical data"]
        H["Human reasoning\nand logic"]
        PS_H[Production Scheduling]
        PT_H[Production Targets]
        PARAMS_H --> H
        H --> PS_H
        PS_H --> PT_H
    end

    subgraph NEW["AI Agent Approach"]
        PARAMS_AI["• Parameters\n• Algorithms\n• Spreadsheets\n• Rules-based apps\n• Historical data"]
        STM_PS["Short-term Memory\nDynamic goals\nConstraints"]
        LTM_PS["Long-term Memory\nProduction manuals\nOperational details"]
        PM_PS["Predictive Model\nForecasts based on\nhistorical data"]
        AI["AI reasoning\nand logic"]
        PS_AI[Production Scheduling]
        PT_AI[Production Targets]
        PARAMS_AI --> AI
        STM_PS --> AI
        LTM_PS --> AI
        PM_PS --> AI
        AI --> PS_AI
        PS_AI --> PT_AI
    end

Example prompt for the Production Scheduling Agent:

Prompt Expression — Production Scheduling Agent
═════════════════════════════════════════════════

You are a production scheduling analyst.

Responsible for:
  → Creating an optimized production plan

Considering (input):
  → Targets/constraints  (short-term memory)
       - Quarterly objectives
       - Equipment constraints
       - Current schedules
  → Domain knowledge     (long-term memory)
       - Production manuals
       - Plant operational details
       - Historical ERP data

To produce (output):
  → Production schedule per line
  → Production targets per line
  → Summary report

Detailed Production Scheduling Agent architecture:

graph TD
    subgraph PSA["Production Scheduling Agent"]
        PG["Prompt Generator"]
        STM["Short-term Memory\nTargets / Constraints\nDynamic"]
        LTM["Long-term Memory\nDomain Knowledge\nManuals & ERP"]
        LLM["Language & Reasoning Model\n(LLM)"]
        PM["Predictive Model\nProduction forecasts\nbased on history"]
        OI["Output Interface"]

        PG --> LLM
        STM --> LLM
        LTM --> LLM
        PM <--> LLM
        LLM --> OI
    end

    INPUT["• Parameters\n• Algorithms\n• Spreadsheets\n• Historical data"] --> PSA
    OI --> SCHED["Production Schedule"]
    OI --> TARGETS["Production Targets"]

3.3 Multi-Agent Collaboration

The multi-agent architecture enables cascading collaboration between specialized agents:

graph TD
    EMAIL_IN["Incoming Email\nEx: supply chain disruption\n(2-week delay)"]

    subgraph FEMA["Factory Email Manager Agent\n(LLM)"]
        EMA_STM[Short-term Memory]
        EMA_LTM[Long-term Memory]
        EMA_OI[Output Interface]
    end

    subgraph FMA["Factory Manager Agent\n(LLM — Chatbot Assistant)"]
        FMA_STM[Short-term Memory]
        FMA_LTM[Long-term Memory]
        FMA_OI[Output Interface]
    end

    subgraph MSA["Maintenance Scheduling Agent\n(LLM + Predictive Model)"]
        MSA_STM[Short-term Memory]
        MSA_LTM[Long-term Memory]
        MSA_OI[Output Interface]
    end

    subgraph PSA["Production Scheduling Agent\n(LLM + Predictive Model)"]
        PSA_STM[Short-term Memory]
        PSA_LTM[Long-term Memory]
        PSA_OI[Output Interface]
    end

    subgraph RA["Reporting Agent\n(LLM)"]
        RA_STM[Short-term Memory]
        RA_LTM[Long-term Memory]
        RA_OI[Output Interface]
    end

    EMAIL_IN --> FEMA
    FEMA --> FMA
    FMA -->|"Updates targets\nand constraints"| MSA
    FMA -->|"Updates targets\nand constraints"| PSA
    MSA <-->|"Exchanges targets\nand constraints"| PSA
    MSA --> RA
    PSA --> RA
    RA --> EMAIL_OUT["Daily reports\nrevised schedules v2/v3/v4\nauto-sent to managers"]
    PSA --> PS_V["Production Schedule\nv2 / v3 / v4"]
    MSA --> MS_V["Maintenance Schedule\nv2 / v3 / v4"]

Concrete example: By simply forwarding an email from a supplier signaling a 2-week supply chain delay, the multi-agent system generates revised maintenance and production schedules in seconds, minimizing downtime.

4. Real-Time Defect Detection

Unlike the previous sections (which dealt with batched and text-based data), AI vision and AI listening agents operate in real time with visual and auditory perception.

4.1 AI Vision Agents

Standard network cameras feed AI vision agents to detect defects as soon as they appear on the production line.

flowchart LR
    CAM["Network cameras\non production\nlines"]

    subgraph AI_V["AI Vision Agent"]
        direction TB
        INPUT_API["Input API\nReal-time video stream"]
        MODEL["AI Vision Model\n(Neural Network)"]
        OUTPUT_API["Output API"]
        INPUT_API --> MODEL
        MODEL --> OUTPUT_API
    end

    CAM --> AI_V
    AI_V --> ALERT["Raise Alert\n(production team)"]
    AI_V --> LOG["Log Details\n(location, time, type)"]
    AI_V --> ACTION["Corrective Actions\n(interaction with other agents)"]
    AI_V --> MSA["Maintenance\nScheduling Agent"]
    AI_V --> PSA["Production\nScheduling Agent"]

AI Vision model types:

4.2 Training Vision Models

flowchart TD
    IMG["Thousands of images\nof products and\nproduction lines"]
    LABEL["Human labeling\n• Normal vs defective\n• Identified objects\n• Precise annotations"]
    AUG["Data Augmentation\n• Duplication\n• Rotation\n• Flipping\n(all orientations)"]
    ML["ML Training"]
    M1["AI Vision Model\n'What is normal'"]
    M2["AI Vision Model\n'Unknown artefacts'"]
    M3["AI Vision Model\n'Placement & position'"]

    IMG --> LABEL
    LABEL --> AUG
    AUG --> ML
    ML --> M1
    ML --> M2
    ML --> M3

Detailed training process:

 Labeled images              ML Training        AI Vision Model
═══════════════════════    ═════════════    ════════════════════════
 • Correct product     ┐                   ┌  "What is normal"
 • Defective product   ┘──► Neural Net ───►└  (Neural Network)

 • Artefact present    ┐                   ┌  "Unknown artefacts"
 • No artefact         ┤──► Neural Net ───►│  (Neural Network)
 • Artefact type       ┘                   └

 • Correct position    ┐                   ┌  "Placement & position"
 • Incorrect position  ┤──► Neural Net ───►│  (Neural Network)
 • Measured offset     ┘                   └

4.3 AI Listening Agents

In addition to vision, AI listening agents detect acoustic anomalies on the production line.

flowchart LR
    MIC["Microphones\non machines\nand production lines"]

    subgraph AI_L["AI Listening Agent"]
        direction TB
        INPUT_API_L["Input API\nReal-time audio stream"]
        MODEL_L["AI Listening Model\n(Neural Network)"]
        OUTPUT_API_L["Output API"]
        INPUT_API_L --> MODEL_L
        MODEL_L --> OUTPUT_API_L
    end

    MIC --> AI_L
    AI_L --> ALERT_L["Raise Alert"]
    AI_L --> LOG_L["Log Location & Time"]
    AI_L --> ACTION_L["Corrective Actions"]

AI Listening model training:

• Trained on audio data from normal production
• Detects subtle changes in machine hum
• Identifies abnormal vibrations in production belts
• Built using the same machine learning techniques as other models

4.4 Complete Real-Time Detection Architecture

graph TD
    subgraph SENSORS["Real-time Sensors"]
        CAM_N["Network cameras"]
        MIC_N["Microphones"]
        TEMP_N["Temperature sensors"]
        LIDAR_N["LiDAR / Radar"]
    end

    subgraph AGENTS["AI Sensor Agents"]
        AV["AI Vision Agent"]
        AL["AI Listening Agent"]
        AT["AI Temperature Agent"]
        AR["AI LiDAR/Radar Agent"]
    end

    subgraph ACTIONS["Autonomous Actions"]
        ALERT_A["Alerts\nProduction teams"]
        LOG_A["Logs\nLocation, time, type"]
        CORRECT_A["Corrections\nAutonomous or guided"]
    end

    CAM_N --> AV
    MIC_N --> AL
    TEMP_N --> AT
    LIDAR_N --> AR

    AGENTS --> ACTIONS
    ACTIONS --> MSA_D["Maintenance\nScheduling Agent"]
    ACTIONS --> PSA_D["Production\nScheduling Agent"]

AI vision and AI listening are just a starting point. Many other sensor types can be monitored in real time by an AI agent capable of detecting anomalies.

5. Continuous Quality Improvement

The same AI vision mechanisms used for defect detection also enable continuous improvement of product quality and processes.

flowchart TD
    CAM_HR["High-resolution cameras\nat end of production line\n(periodic images for inspection)"]

    subgraph QA_AGENTS["AI Quality Inspection Agents"]
        direction LR
        QC1["Final product finish\n& quality vs ideal references"]
        QC2["Automatic counting\nof specific elements\n(e.g.: raisins on a cake)"]
        QC3["Raw material waste\ndetection on lines"]
        QC4["Resource utilization\nefficiency monitoring"]
    end

    CAM_HR --> QA_AGENTS

    QA_AGENTS --> LOG_Q["Local log\nof quality issues"]
    LOG_Q --> REVIEW["Continuous review\nby production teams\n(continuous improvement)"]
    LOG_Q --> MSA_Q["Maintenance\nScheduling Agent\n(Short-term Memory)"]
    LOG_Q --> PSA_Q["Production\nScheduling Agent\n(Short-term Memory)"]
    MSA_Q --> SCHED_Q["Revised schedules\nintegrating continuous\nimprovement"]
    PSA_Q --> SCHED_Q

Concrete use cases:

High-resolution images allow multiple specialized agents to simultaneously inspect different zones of the same product — each zone containing enough detail for fine-grained analysis.

Integration into the Multi-Agent System

graph LR
    QA_R["Quality Check\nAI Agents"]
    MSA_R["Maintenance\nScheduling Agent\n(Short-term Memory)"]
    PSA_R["Production\nScheduling Agent\n(Short-term Memory)"]
    SCHED_R["Continuously\nimproved\nschedules"]

    QA_R -->|"Quality issue\nsummary"| MSA_R
    QA_R -->|"Quality issue\nsummary"| PSA_R
    MSA_R -->|"Improvement\nprioritization"| SCHED_R
    PSA_R -->|"Improvement\nprioritization"| SCHED_R

6. AI Costs vs. Production Cost Reduction

6.1 Implementation Costs

Introducing AI agents is not free. Here are the main cost categories:

╔══════════════════════════════════════════════════════════════╗
║                  AI Implementation Costs                     ║
╠══════════════════════════════╦═══════════════════════════════╣
║      INITIAL COSTS           ║      ONGOING COSTS            ║
╠══════════════════════════════╬═══════════════════════════════╣
║ • AI data infrastructure     ║ • Agent maintenance           ║
║ • Agent setup                ║ • Model tuning                ║
║ • Dedicated engineer time    ║ • Continuous experimentation  ║
║ • Team training              ║ • New data collection         ║
║                              ║ • Data labeling               ║
╠══════════════════════════════╬═══════════════════════════════╣
║  SPECIFIC EFFORT             ║  SHORT-TERM RISKS             ║
╠══════════════════════════════╬═══════════════════════════════╣
║ • Audio and visual data      ║ • Costs > benefits initially  ║
║   collection                 ║ • Temporary uptime reduction  ║
║ • Data labeling              ║ • Error rate to be adjusted   ║
║ • Model validation           ║ • Resistance to change        ║
╚══════════════════════════════╩═══════════════════════════════╝

Agents and domains covered by the investment:

graph LR
    subgraph AGENT_TYPES["AI Agent Types"]
        MSA_C["Maintenance\nScheduling Agent"]
        RA_C["Reporting Agent"]
        PSA_C["Production\nScheduling Agent"]
        AVA_C["AI Vision Agent(s)"]
        ALA_C["AI Listening Agent(s)"]
        ATA_C["AI Temperature Agent"]
        ALRA_C["AI LiDAR/Radar Agent"]
        FMA_C["Factory Manager\nAgent"]
        FEMA_C["Factory Email\nManager Agent"]
    end

    subgraph BENEFITS_C["Benefits"]
        MAINT_C["Maintenance\nScheduling"]
        PROD_C["Production\nScheduling"]
        DEFECT_C["Defect\nDetection"]
        QUALITY_C["Quality\nImprovement"]
        PRODUCTION_C["Production ↑"]
        UPTIME_C["Uptime ↑"]
    end

    MSA_C --> MAINT_C
    RA_C --> MAINT_C
    PSA_C --> PROD_C
    AVA_C --> DEFECT_C
    ALA_C --> DEFECT_C
    ATA_C --> DEFECT_C
    ALRA_C --> DEFECT_C
    AVA_C --> QUALITY_C
    FMA_C --> PROD_C
    FEMA_C --> PROD_C
    MAINT_C --> PRODUCTION_C
    PROD_C --> PRODUCTION_C
    DEFECT_C --> UPTIME_C
    QUALITY_C --> UPTIME_C

6.2 AI Hallucinations

Critical point: AI models can be incorrect 10% of the time or more — this is known as AI hallucinations.

    Managing AI Hallucinations
    ═══════════════════════════════════════════════════════════
    
     Agents                    Potential error rate
    ┌──────────────────────┐   ┌────────────────────────┐
    │ Maintenance Agent    │──►│        10% ?           │
    ├──────────────────────┤   ├────────────────────────┤
    │ Production Agent     │──►│        10% ?           │
    ├──────────────────────┤   ├────────────────────────┤
    │ AI Vision Agent(s)   │──►│        10% ?           │
    ├──────────────────────┤   ├────────────────────────┤
    │ AI Listening Agent(s)│──►│        10% ?           │
    ├──────────────────────┤   ├────────────────────────┤
    │ Reporting Agent      │──►│        10% ?           │
    └──────────────────────┘   └────────────────────────┘

    Solutions:
    ──────────────────────────────────────────────────────────
    ✔ AI data and model tuning
    ✔ Complementary quality checks
    ✔ Parallel testing with manual methods
    ✔ Human validation on critical decisions
    ✔ Dedicated AI hallucination detection solutions

This error rate cannot be completely eliminated, but it can be:

• Reduced through progressive tuning of data and models
• Managed through complementary quality checks
• Continuously monitored by teams
• Mitigated by parallel testing with existing manual methods

6.3 Cost-Benefit Analysis

graph TD
    subgraph COSTS_SIDE["Costs"]
        direction TB
        C1["AI data infrastructure\n(initial setup)"]
        C2["Data and AI model\ntuning"]
        C3["Data collection\nand labeling"]
        C4["Anti-hallucination solutions\nand quality checks"]
        C5["High initial time\nand cost"]
    end

    subgraph BENEFITS_SIDE["Expected Benefits"]
        direction TB
        B1["Optimized and proactive\nmaintenance scheduling"]
        B2["Optimized and accurate\nproduction scheduling"]
        B3["Real-time\ndefect detection"]
        B4["Continuous\nquality improvement"]
        B5["Production ↑\nand Uptime ↑"]
        B6["Teams freed\nfor high human-value\ntasks"]
    end

    COSTS_SIDE -->|"Initial\ninvestment"| INFLECTION["Inflection Point\n(after tuning and validation)"]
    INFLECTION -->|"Progressive\nbenefits"| BENEFITS_SIDE

When AI agents are well-calibrated, the result seems almost magical: production teams save enormous amounts of time and money, while seeing increases in uptime and throughput.

6.4 Recommended Deployment Strategy

flowchart LR
    P1["Phase 1\nParallel testing\nAI + manual methods"]
    P2["Phase 2\nValidation\nResults comparison"]
    P3["Phase 3\nProgressive deployment\nReplacing manual processes"]
    P4["Phase 4\nOngoing maintenance\nTuning and experimentation"]

    P1 --> P2
    P2 --> P3
    P3 --> P4
    P4 -->|"Continuous\nimprovement"| P4

There is no reason not to test the AI system in parallel with existing manual methods, until you have complete confidence in the produced schedules and targets.

7. Summary — Complete Multi-Agent System

Final Architecture Overview

graph TD
    subgraph DATA["Input Data"]
        SD_F["Sensor Data\n(real-time)"]
        RL_F["Repair Logs\n(history)"]
        VD_F["Visual Data\n(cameras)"]
        AD_F["Audio Data\n(microphones)"]
        ERP_F["ERP / Supply Chain\n(emails, systems)"]
    end

    subgraph SENSOR_AGENTS_F["AI Sensor Agents (real-time)"]
        AVA_F["AI Vision\nAgent(s)"]
        ALA_F["AI Listening\nAgent(s)"]
        ATA_F["AI Temperature\nAgent"]
        ALRA_F["AI LiDAR/Radar\nAgent"]
    end

    subgraph CORE_AGENTS_F["AI Core Agents (planning)"]
        MSA_F["Maintenance\nScheduling Agent\n(LLM + Predictive Model)"]
        PSA_F["Production\nScheduling Agent\n(LLM + Predictive Model)"]
        RA_F["Reporting Agent\n(LLM)"]
        FMA_F["Factory Manager\nAgent (LLM)"]
        FEMA_F["Factory Email\nManager Agent (LLM)"]
    end

    subgraph OUTPUTS_F["Outputs"]
        MAINT_F["Maintenance\nSchedule"]
        PROD_F["Production\nSchedule"]
        REPORTS_F["Auto-generated\nReports"]
        ALERTS_F["Real-time\nAlerts"]
        QUALITY_F["Quality\nLogs"]
    end

    SD_F --> MSA_F
    RL_F --> MSA_F
    VD_F --> AVA_F
    AD_F --> ALA_F
    ERP_F --> FEMA_F

    SENSOR_AGENTS_F --> CORE_AGENTS_F
    SENSOR_AGENTS_F --> ALERTS_F
    SENSOR_AGENTS_F --> QUALITY_F

    FEMA_F --> FMA_F
    FMA_F --> MSA_F
    FMA_F --> PSA_F
    MSA_F <-->|"Exchanges targets\nand constraints"| PSA_F
    MSA_F --> RA_F
    PSA_F --> RA_F

    MSA_F --> MAINT_F
    PSA_F --> PROD_F
    RA_F --> REPORTS_F

Benefits Summary Table

Summary of Key Components

╔══════════════════════════════════════════════════════════════╗
║                  AI Agent Components                         ║
╠══════════════════════════════════════════════════════════════╣
║  Prompt Generator API   Transforms events into prompts       ║
║  Short-term Memory      Dynamic goals & context              ║
║  Long-term Memory       Durable business knowledge           ║
║  LLM Core               Reasoning & comprehension            ║
║  Predictive Model       Forecasts based on patterns          ║
║  Output Interface API   Actions: tools, APIs, agents         ║
╚══════════════════════════════════════════════════════════════╝

╔══════════════════════════════════════════════════════════════╗
║              Agents in the Complete Ecosystem                ║
╠══════════════════════════════════════════════════════════════╣
║  Maintenance Scheduling Agent  (LLM + Predictive Model)      ║
║  Production Scheduling Agent   (LLM + Predictive Model)      ║
║  Reporting Agent               (LLM)                         ║
║  Factory Manager Agent         (LLM — Chatbot assistant)     ║
║  Factory Email Manager Agent   (LLM — Email interface)       ║
║  AI Vision Agent(s)            (Vision models)               ║
║  AI Listening Agent(s)         (Audio models)                ║
║  AI Temperature Agent          (Temperature models)          ║
║  AI LiDAR/Radar Agent          (LiDAR/Radar models)          ║
╚══════════════════════════════════════════════════════════════╝

Search Terms

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