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Challenge

Build a High-Trust Public Service Agent with Claude Agents SDK and Human-in-the-Loop

Build a high-trust public service automation workflow with human-in-the-loop review gates for Singapore Smart Nation applications. Powered by Claude Agents SDK, your system will process citizen benefits requests, evaluate eligibility rules, and route exceptions for human approval.

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Challenge brief

What you are building

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

Develop a high-trust public service agent with Claude Agents SDK featuring explicit human-in-the-loop governance for civic benefit distributions.

Delivery guide

How work is evaluated

Evaluation

Evaluate decision correctness and human approval routing accuracy for civic benefit applications.

Datasets

Shared data for this challenge

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

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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

hitl_trigger_correctness

Correctly flags special circumstances for human review

Binary check

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

Dimension 2

eligibility_precision

Precision of automated benefit approvals • target: 0.96 • 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

  • Implement Claude Agents SDK workflows with strict safety constraints and audit logs

  • Build human-in-the-loop approval handoffs for edge-case eligibility scenarios

  • Ensure compliance with Singapore Smart Nation ethical AI governance standards

  • Design zero-hallucination policy compliance tools for government benefit distribution

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic citizen benefit application dossiers containing income levels, property valuations, and policy edge cases.

How this agent runs

Evaluate decision correctness and human approval routing accuracy for civic benefit applications.

Challenge input

JSON with applicant_id, household_income, property_pree_val, special_circumstances

Claude Agents SDK

Anthropic SDK supporting extended reasoning and structured tool interaction.

Evaluated output

JSON with eligible (bool), requires_human_review (bool), decision_reason

Checks for
  • Correctly flags special circumstances for human review
  • Precision of automated benefit approvals • target: 0.96 • range: 0-1
Proof of success
  • Benchmark: GovTech_Public_Trust_Bench
  • Eligibility Precision target: 0.96
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • Ludwig · Optional
  • Embedchain · Optional
Policy Serving
  • OpenRouter · Required
Orchestration
  • OpenRouter · Required

Evaluation contract

  • hitl_trigger_correctness · Weight 1
  • eligibility_precision · Weight 1

Recipe state

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

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