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Crypto Compliance Agent: Detecting Anomalous Transactions with Gemini 3 Pro Deep Think

This challenge requires you to build a sophisticated compliance monitoring agent using LangGraph. Your system will employ a graph-based workflow to analyze a stream of simulated cryptocurrency transactions, integrating MCP-enabled tools for external blockchain data lookups and historical pattern analysis. Gemini 3 Pro, operating in Deep Think mode, will be crucial for performing hybrid reasoning to interpret complex transaction sequences and flag potentially illicit activities that evade simpler rule-based detection.

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What you are building

Develop a LangGraph-based system using Gemini 3 Pro Deep Think and MCP tool integration to detect anomalous cryptocurrency transactions.

How work is evaluated

Evaluation

The evaluation will assess the LangGraph system's ability to accurately detect a predefined set of anomalous transaction patterns within a simulated stream, along with the quality of its explanations.

Datasets

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

Learning goals

  • Design a stateful, graph-based agent workflow using LangGraph.

  • Integrate Gemini 2.5 Pro's Deep Think mode for complex pattern recognition.

  • Implement MCP-enabled tools for real-time blockchain data access and historical analysis.

  • Generate alerts and detailed explanations for suspicious activities.

  • Develop a hybrid reasoning system to identify multi-step anomalous transaction patterns.

Resources

Dataset notes

Sample data for 1 tasks

Challenge setup

The evaluation will assess the LangGraph system's ability to accurately detect a predefined set of anomalous transaction patterns within a simulated stream, along with the quality of its explanations.

Challenge input

{'transactions': [{'id': 'tx1', 'from': 'addr_a', 'to': 'addr_b', 'amount': 100, 'timestamp': '...', 'type': 'transfer'}]}

Agent execution

No agent tool is listed for this challenge.

Evaluated output

{'anomalies': [{'tx_id': 'tx5', 'reason': 'Circular transaction pattern detected', 'severity': 'high', 'confidence': 0.95}]}

Evaluation checks
  • Each detected anomaly must include 'tx_id', 'reason', 'severity', 'confidence', and 'chain_of_thought'.
  • The system must achieve a minimum recall of 75% on the provided anomalous transaction patterns.
Configured evaluation evidence
  • Precision target: 0.85
Configured runtime
  • Python execution harness

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • Each detected anomaly must include 'tx_id', 'reason', 'severity', 'confidence', and 'chain_of_thought'.
  • The system must achieve a minimum recall of 75% on the provided anomalous transaction patterns.

Metrics

  • Precision · Target 0.85
  • ReasoningDepthScore · Target 4

Workflow tasks

  1. Task 1

    Input: {'transactions': [{'id': 'tx1', 'from': 'addr_a', 'to': 'addr_b', 'amount': 100, 'timestamp': '...', 'type': 'transfer'}]}

    Output: {'anomalies': [{'tx_id': 'tx5', 'reason': 'Circular transaction pattern detected', 'severity': 'high', 'confidence': 0.95}]}

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Crypto Compliance Agent: Detecting Anomalous Transactions with Gemini 3 Pro Deep Think