Build an AI-Powered Regulatory Compliance Risk Agent
Modern enterprises face complex legal and regulatory landscapes, particularly concerning data sovereignty and privacy. This challenge involves developing an autonomous agent designed to assess potential compliance risks for a multinational corporation, specifically focusing on data storage regulations, cross-border data transfer policies, and the implications of governmental legal orders on encrypted data. The agent will leverage advanced reasoning capabilities to interpret legal texts, identify potential vulnerabilities, and recommend mitigation strategies. The solution will utilize the OpenAI Agents SDK to orchestrate tool use, manage conversational state, and enable the agent to interact with a simulated legal database and a policy evaluation framework. The agent should be capable of understanding nuanced legal language and providing actionable insights for legal and compliance teams.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an OpenAI Agents SDK-powered agent for real-time regulatory compliance risk assessment and policy analysis.
How work is evaluated
The agent will be evaluated on its ability to accurately identify regulatory risks, propose relevant mitigation strategies, and demonstrate robust tool usage and reasoning.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Syntactic Correctness
Output adheres to specified JSON format.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Legal Accuracy (Key Points)
Identified risks and mitigations align with standard legal interpretations of provided regulations for simple cases.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Tool Invocation
Agent demonstrates correct invocation of at least one defined tool.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Risk Identification Completeness
Percentage of relevant risks identified. • target: 90 • range: 0-100
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Mitigation Strategy Relevance
Average relevance score of proposed mitigations. • target: 4.5 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Reasoning Coherence Score
Internal coherence and logical flow of the agent's explanation. • target: 4 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master the OpenAI Agents SDK for defining agent capabilities, tool definitions, and conversation management, including state persistence.
Implement dynamic tool integration with the OpenAI Agents SDK to interact with a simulated legal knowledge base and internal policy documents.
Design a multi-step reasoning process using Claude Opus 4.1 through the OpenAI Assistants API for legal interpretation, risk identification, and mitigation strategy generation.
Integrate Giskard for evaluating the agent's policy compliance recommendations against predefined legal standards and enterprise policies.
Configure agent parameters and tool definitions using Hydra for robust and version-controlled experimental setups.
Explore using DeepSeek R1 via a custom tool for fast, contextual information retrieval from large legal corpora.
Build a secure environment for processing sensitive legal information, adhering to best practices for data privacy and access control.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The agent will be evaluated on its ability to accurately identify regulatory risks, propose relevant mitigation strategies, and demonstrate robust tool usage and reasoning.
Challenge input
{'scenario': 'string', 'regulations': ['string']}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'risks': [{'description': 'string', 'severity': 'low|medium|high', 'regulatory_basis': 'string'}], 'mitigations': ['string']}
- Output adheres to specified JSON format.
- Identified risks and mitigations align with standard legal interpretations of provided regulations for simp...
- Agent demonstrates correct invocation of at least one defined tool.
- Risk Identification Completeness target: 90
- 2 public reference cases
- Python execution harness
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
No tool records are attached.
Evaluation contract
- Syntactic Correctness · Weight 1
- Legal Accuracy (Key Points) · Weight 1
- Tool Invocation · Weight 1
- Risk Identification Completeness · Weight 1
- Mitigation Strategy Relevance · Weight 1
- Reasoning Coherence Score · Weight 1
Metrics
- Risk Identification Completeness · Target 90
- Mitigation Strategy Relevance · Target 4.5
- Reasoning Coherence Score · Target 4
Reference cases
- 2 public reference cases
Workflow tasks
- Task 1
Input: {'scenario': 'string', 'regulations': ['string']}
Output: {'risks': [{'description': 'string', 'severity': 'low|medium|high', 'regulatory_basis': 'string'}], 'mitigations': ['string']}
Execution harness: Python
- Task 2
Input: {'query': 'string'}
Output: {'tool_calls': [{'tool_name': 'string', 'arguments': 'dict'}], 'response': 'string', 'errors_detected': 'boolean'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.