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Challenge

Power Plant EPC Milestone Monitor with Pydantic AI & Hamming

Contractors confirmed a $683m combined-cycle gas turbine (CCGT) power plant contract in Oman. Build a structured Pydantic AI agent monitored by Hamming to extract contractual milestones from construction logs, identify delay risks, and trigger mitigation tasks. Achieve 90%+ extraction accuracy on vendor progress reports.

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Challenge brief

What you are building

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

Develop a type-safe Pydantic AI agent with Hamming evaluation tracking to extract construction milestones and forecast project delay risks.

Delivery guide

How work is evaluated

Evaluation

Evaluates milestone extraction accuracy and delay risk detection from construction logs.

Datasets

Challenge datasets

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

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

schema_validation

Checks if output conforms strictly to Pydantic model

Binary check

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

Dimension 2

field_extraction_accuracy

Precision of delay days and milestone name extraction • target: 0.9 • 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

  • Define strict Pydantic schemas for EPC construction milestones and deliverables.

  • Build a Pydantic AI agent that parses unstructured contractor status reports.

  • Integrate Hamming for continuous prompt debugging and automated evaluation metrics.

  • Implement delay detection logic based on critical path dependencies.

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic contractor status reports modeled after Middle Eastern combined-cycle plant construction logs.

Challenge setup

Evaluates milestone extraction accuracy and delay risk detection from construction logs.

Challenge input

Raw text log from site engineers

Pydantic AI

Provides type-safe structured parsing.

Hamming

Ensures prompt consistency and extraction metrics tracking.

Evaluated output

JSON matching EPCMilestone Pydantic model

Evaluation checks
  • Checks if output conforms strictly to Pydantic model
  • Precision of delay days and milestone name extraction • target: 0.9 • range: 0-1
Configured evaluation evidence
  • Benchmark: ExtractBench-EPC
  • Field Extraction Accuracy target: 0.9
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Hamming · Required
  • Pydantic AI · Optional
  • Lindy · Optional
Orchestration
  • Pydantic AI · Optional

Evaluation contract

  • schema_validation · Weight 1
  • field_extraction_accuracy · Weight 1

Metrics

  • field_extraction_accuracy · Target 0.9

Benchmarks

  • ExtractBench-EPC
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Raw text log from site engineers

    Output: JSON matching EPCMilestone Pydantic model

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework
  • Hamming · Evaluations & Observability

Recipe freeze marker

No recipe freeze time is recorded.

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