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Industrial Operations Capture Analysis with Mastra AI and Claude 4 Opus

Inconsistent data capture across plant operations causes widespread MES and ERP state synchronization failures. Build a data ingestion and capture fault analyzer using Mastra AI framework and Claude 4 Opus to automatically isolate data loss causes and achieve over 98% capture reliability.

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What you are building

Construct an enterprise data capture diagnostic agent using Mastra AI and Claude 4 Opus to resolve MES/SCADA logging errors.

How work is evaluated

Evaluation

Evaluates Mastra AI agent's ability to clean corrupted historian logs and fix capture bottlenecks.

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

sanitization_validity

Ensures all output sanitized_records contain clean numeric fields.

Binary check

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

Dimension 2

capture_recovery_rate

Percentage of corrupted historical records successfully recovered • target: 0.98 • 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

  • Configure Mastra AI workflows to monitor plant data historian ingestion queues

  • Use Claude 4 Opus to identify malformed JSON-LD payload structures from OPC bridges

  • Build automated data sanitization and retry loops within Mastra agent tools

  • Track end-to-end data throughput and capture success rates

Resources

Dataset notes

Historical plant historian capture logs containing truncation, format corruption, and buffer noise.

Challenge setup

Evaluates Mastra AI agent's ability to clean corrupted historian logs and fix capture bottlenecks.

Challenge input

JSON array of raw records with timestamp, source_id, raw_string

Mastra AI

TypeScript agentic framework for workflow orchestration

Claude 4 Opus

High-capability reasoning model for complex schema normalization

Evaluated output

JSON object with sanitized_records (array), errors_rectified (int), cause (string)

Evaluation checks
  • Ensures all output sanitized_records contain clean numeric fields.
  • Percentage of corrupted historical records successfully recovered • target: 0.98 • range: 0-1
Configured evaluation evidence
  • Benchmark: PlantData-Capture-Bench
  • Capture Recovery Rate target: 98%
  • 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
  • Claude Code · Optional
Observation
  • Claude Code · Optional
Policy Serving
  • Claude 4 Opus · Optional
Orchestration
  • Mastra AI · Required

Evaluation contract

  • sanitization_validity · Weight 1
  • capture_recovery_rate · Weight 1

Metrics

  • capture_recovery_rate · Target 0.98

Benchmarks

  • PlantData-Capture-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of raw records with timestamp, source_id, raw_string

    Output: JSON object with sanitized_records (array), errors_rectified (int), cause (string)

    Execution harness: JavaScript

Evaluation tools

  • Mastra AI · agent_framework
  • Claude 4 Opus · llm_model

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Industrial Operations Capture Analysis with Mastra AI and Claude 4 Opus