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.
What you are building
The core problem, expected build, and operating context for this challenge.
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.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building complex, stateful Directed Acyclic Graph (DAG) workflows that manage multi-agent interactions and tool calls.
Implement Gemini 3 Pro in Deep Think mode for advanced, multi-stage reasoning to identify subtle anomalies in large datasets.
Design and integrate MCP-enabled tools to interact with simulated blockchain explorers and internal compliance databases for data enrichment.
Develop hybrid reasoning strategies that combine instant pattern matching with deep, contextual analysis of transaction histories and user behaviors.
Orchestrate a pipeline for ingesting simulated real-time transaction streams and applying your LangGraph analysis.
Build a feedback loop mechanism for the LangGraph agents to learn and refine anomaly detection thresholds or patterns over time.
Deploy robust error handling and checkpointing within the LangGraph workflow for resilient compliance monitoring.
How this agent runs
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
The configured agent processes the input under the challenge policy.
Evaluated output
{'anomalies': [{'tx_id': 'tx5', 'reason': 'Circular transaction pattern detected', 'severity': 'high', 'confidence': 0.95}]}
- 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.
- Precision target: 0.85
- Python execution harness
View technical recipe
Configured tools
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Evaluation contract
- The evaluation module defines the checks.
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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