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Challenge

Build a Work-Sample Assessment Review Assistant

Build an agent that organizes work samples, maps evidence to rubric criteria, and drafts review notes. Let human reviewers inspect the work, adjust criterion judgments, and publish their own feedback.

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Challenge brief

What you are building

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

A reviewer assessing submitted technical work against a published rubric.

Build an agent that organizes work samples, maps evidence to rubric criteria, and drafts review notes. Let human reviewers inspect the work, adjust criterion judgments, and publish their own feedback.

Synthetic work submissions

Published job-relevant rubric

Reviewer comments

Remove irrelevant identity fields from the review view.

Extract evidence for each rubric criterion.

Draft criterion-level judgments and clarification questions.

Collect human edits and export an evidence-linked feedback report.

A reviewer workspace with the submission, criterion evidence, editable judgments, and a feedback preview.

Draft five points for R1 and zero for R2 with citations to the work, then let a reviewer edit the feedback.

A working local application with the specified review interface.

Source code, dependency lockfile, and setup commands.

A recorded end-to-end demo using the supplied starter data.

Example outputs, action logs, and automated tests.

These fictional records are a small development fixture, not a production dataset. Implement the mock services described in the brief.

```json { "submission": { "id": "S1", "synthetic_name": "Person A", "text": "The solution returns the correct total. It includes no tests." }, "rubric": [ { "id": "R1", "criterion": "Correct result", "maximum_points": 5 }, { "id": "R2", "criterion": "Regression tests", "maximum_points": 5 } ], "evidence": { "correct_result": true, "tests_present": false } } ```

Use synthetic work. Keep judgments reviewable and do not automate real employment decisions. Choose an available model that supports your implementation. Record its identifier and version.

Review the complete application, not only the starter-case answers.

The draft total is five and each judgment has evidence.

Identity fields are absent from the scoring view.

No hiring decision or applicant ranking is generated. Add one normal case, one incomplete-input case, and one failure-recovery case.

Rules

Participation guide

Use synthetic work. Keep judgments reviewable and do not automate real employment decisions.

Delivery guide

How work is evaluated

Evaluation

Review workflow completion, interface usability, evidence quality, and safe failure handling. No agent execution has been verified.

Submission format

A working local application with the specified review interface. Source code, dependency lockfile, and setup commands. A recorded end-to-end demo using the supplied starter data. Example outputs, action logs, and automated tests.

Datasets

Challenge datasets

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

Learning goals

What you should walk away with

  • Remove irrelevant identity fields from the review view.

  • Extract evidence for each rubric criterion.

  • Draft criterion-level judgments and clarification questions.

  • Collect human edits and export an evidence-linked feedback report.

Resources and assets

Reference links and supporting material

Dataset notes

Supplied fictional starter records: Synthetic work submissions, Published job-relevant rubric, Reviewer comments. The overview includes the complete JSON bundle. Implement local mock services and extend the sample coverage.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Pydantic AI

Typed Python agent framework.

Evaluated output

No output checks are configured.

Evaluation checks
  • No evaluation checks are configured.
Configured evaluation evidence
  • 1 public reference case
Configured runtime
  • 1 configured tool

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

Orchestration
  • Pydantic AI ยท Optional

Evaluation contract

  • No evaluation checks are configured.

Reference cases

  • 1 public reference case

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Work-Sample Assessment Review Assistant