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Five Agents Five production priorities.

Generation 1 is built to observe, monitor, predict, analyze, and investigate.

The first Connected Manufacturing Agents observe, monitor, investigate, and surface insight across the drivers of production performance that matter most.

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Quinn — Quality Defect Agent

Outcome: Improve quality.

Quinn monitors incoming materials and production output for quality issues. Ask Quinn when elevated defect or rejection rates appear and the team needs to understand why.

Quinn helps teams:

  • Track defect and rejection trends
  • Identify contributing equipment, materials, or process steps
  • Flag early warning signs before a small issue becomes a larger one

Example questions:

  • What defects increased in the last 24 hours?
  • Which lines are driving the top defect types this week?
  • What may be contributing to the increase?
Demo note:

In a rising defect-rate scenario, Quinn can surface increased defects, drill into likely contributors such as a tool, supplier batch, or shift change, and recommend where teams should focus investigation next.

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Mihir (1) ChatGPT Image May 8, 2026, 06_39_53 AM

Mihir — Yield Monitor Agent

Outcome: Maximize yield.

Mihir watches batch and process yield across production lines. Ask Mihir when output is lower than expected and the team needs to understand where yield is being lost.

Mihir helps teams:

  • Track yield by line, shift, product, or time period
  • Highlight contributing factors such as recipe deviations or input variability
  • Compare current performance against historical norms

Example questions:

  • Why is yield lower on Line 2 than target this month?
  • Which product or line is driving the biggest yield loss?
  • How does this week’s yield compare to historical performance?
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Malone — Tool Failure Agent

Outcome: Reduce downtime.

Malone uses production and MES data to detect early signs of tool or equipment degradation before it causes a stoppage.

Malone helps teams:

  • Identify unusual patterns in machine performance data
  • Flag tools or assets that may need attention soon
  • Help maintenance teams prioritize before failures occur

Example questions:

  • Are any tools showing early warning signs right now?
  • Which assets are trending toward failure?
  • What equipment should maintenance prioritize this week?
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Iona — Downtime Agent

Outcome: Improve uptime.

Iona tracks unplanned downtime events and works to determine what caused them. Ask Iona when equipment has stopped unexpectedly or when teams need to understand downtime trends.

Iona helps teams:

  • Log and classify downtime events
  • Trace contributing factors across equipment, process, and environment
  • Summarize downtime history by shift, line, or time range

Example questions:

  • What caused the most downtime last week?
  • Which shift had the worst downtime yesterday?
  • Has this happened before on this line?
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Riley — Root Cause Analysis Agent

Outcome: Accelerate root-cause analysis.

Riley is the on-demand investigator. Unlike the other agents that run on a schedule, Riley is activated through chat when a team needs a deeper investigation into a specific problem.

Riley helps teams:

  • Perform structured root-cause analysis on demand
  • Draw on the full knowledge graph across data, documents, and history
  • Investigate a specific incident, time window, or symptom

Example questions:

  • Investigate the defect spike on Line 3.
  • There was a quality spike yesterday afternoon — what caused it?
  • What evidence supports the likely root cause?
Demo note:

Riley connects quality defects, yield drops, tool issues, and downtime signals to identify likely root causes and recommend next steps.

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