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Challenge

Autonomous Macro-Economic Inflation Analytics Agent with Google ADK and aiXplain

MAS consumer price reports highlight persistent macroeconomic volatility requiring rapid dynamic risk assessment. Build an autonomous macroeconomic reporting pipeline using Google ADK and aiXplain to digest monthly CPI metrics and emit automated risk scores. Deliver continuous, auditable economic intelligence feeds with zero policy execution drift.

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Challenge brief

What you are building

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

Build a macroeconomic risk monitoring agent using Google ADK and aiXplain workflows to auto-evaluate MAS consumer price index releases.

Delivery guide

How work is evaluated

Evaluation

Evaluates accuracy of CPI risk extraction and structured response schema adherence.

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

Verifies that the output JSON matches the target compliance schema.

Binary check

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

Dimension 2

risk_score_accuracy

Absolute difference between calculated risk score and ground truth. • target: 0.95 • 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 multi-agent workflow orchestration using Google ADK

  • Integrate aiXplain text and tabular translation pipelines

  • Parse and analyze MAS consumer price index time-series datasets

  • Generate structured policy compliance risk summaries

Resources and assets

Reference links and supporting material

Dataset notes

Simulated time-series dataset of MAS Consumer Price Index releases spanning 2024-2026 including itemized YoY changes.

Challenge setup

Evaluates accuracy of CPI risk extraction and structured response schema adherence.

Challenge input

JSON containing CPI statistical breakdown

Google ADK

Required framework for building autonomous agents.

aiXplain

Required utility tool for specialized translation and sentiment extraction.

Evaluated output

JSON with extracted fields and overall score

Evaluation checks
  • Verifies that the output JSON matches the target compliance schema.
  • Absolute difference between calculated risk score and ground truth. • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Financial QA & Numerical Reasoning Benchmark
  • Risk Score Accuracy target: 0.95
  • 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
  • aiXplain · Required
  • Google ADK · Optional
  • Google · Optional

Evaluation contract

  • schema_validation · Weight 1
  • risk_score_accuracy · Weight 1

Metrics

  • risk_score_accuracy · Target 0.95

Benchmarks

  • Financial QA & Numerical Reasoning Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing CPI statistical breakdown

    Output: JSON with extracted fields and overall score

    Execution harness: Python

Evaluation tools

  • Google ADK · Agent Architecture Framework
  • aiXplain · AI Pipeline Orchestrator

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Autonomous Macro-Economic Inflation Analytics Agent with Google ADK and aiXplain