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Challenge

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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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

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

Delivery guide

How work is evaluated

Evaluation

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

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

What you should walk away with

  • 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 and assets

Reference links and supporting material

Dataset notes

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

How this agent runs

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)

Checks for
  • Ensures all output sanitized_records contain clean numeric fields.
  • Percentage of corrupted historical records successfully recovered • target: 0.98 • range: 0-1
Proof of success
  • Benchmark: PlantData-Capture-Bench
  • Capture Recovery Rate target: 98%
  • 1 public reference case
Runtime evidence
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)
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

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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