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Industry 4.0 Factory Scaling Evaluator with Mastra AI

Scaling Industry 4.0 pilots across multiple plant locations fails due to heterogeneous data formats and unstandardized metrics. Build an automated factory scaling agent with Mastra AI to normalize edge telemetry and evaluate deployment readiness. Achieve complete schema alignment across multi-plant OPC UA servers.

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What you are building

Build a Mastra AI workflow to audit multi-plant data schemas and assess readiness for enterprise Industry 4.0 rollouts.

How work is evaluated

Evaluation

Verify Mastra AI agent workflow output for plant telemetry standardization and readiness score calculation.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

schema_normalization_pass

Correctly maps raw plant tag names to canonical forms

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

readiness_score_accuracy

Accuracy of calculated readiness index • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Create a Mastra AI project with agents and workflows for plant schema evaluation

  • Normalize heterogeneous OPC UA and MQTT telemetry data payloads

  • Compute factory digital readiness metrics using structured agent steps

  • Generate multi-plant gap analysis reports

Resources

Dataset notes

Raw tag exports from 10 different factory sites with varying tag naming schemes.

Challenge setup

Verify Mastra AI agent workflow output for plant telemetry standardization and readiness score calculation.

Challenge input

JSON containing plant_id, raw_tags, and connectivity_type

Mastra AI

TypeScript framework for modular AI workflows.

Evaluated output

JSON with normalized_tags, readiness_score, and missing_requirements

Evaluation checks
  • Correctly maps raw plant tag names to canonical forms
  • Accuracy of calculated readiness index • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: ISA-95 Schema Normalization Benchmark
  • Readiness Score Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Mastra AI · Required
Orchestration
  • Mastra AI · Required

Evaluation contract

  • schema_normalization_pass · Weight 1
  • readiness_score_accuracy · Weight 1

Metrics

  • readiness_score_accuracy · Target 0.95

Benchmarks

  • ISA-95 Schema Normalization Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing plant_id, raw_tags, and connectivity_type

    Output: JSON with normalized_tags, readiness_score, and missing_requirements

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · Agentic Workflow Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Industry 4.0 Factory Scaling Evaluator with Mastra AI