Ethics Compliance Agent for Financial Market Analysis
Develop a cutting-edge multi-agent system using Microsoft AutoGen to perform real-time ethical and regulatory compliance analysis for financial market activities. Inspired by recent headlines regarding market integrity rules, this system will leverage a team of specialized agents to interpret complex market news, cross-reference legal frameworks (such as insider trading regulations), and flag potential violations. Qwen 3 235B will power the core reasoning capabilities for the agents, enabling nuanced understanding of verbose financial texts and intricate regulatory guidelines. The solution emphasizes modern AI interaction and evaluation. It should integrate seamlessly with Fixie for natural language voice interactions, allowing financial stakeholders to query compliance statuses and receive immediate alerts or summaries through conversational interfaces. Patronus AI will be employed for robust evaluation and monitoring of the agents' behavior, ensuring strict adherence to compliance policies, identifying any biases, or detecting errors in their decision-making processes. Furthermore, a Wix dashboard will be created to visualize compliance reports, agent decision-making rationales, and overall system performance, providing transparency and auditability.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop a cutting-edge multi-agent system using Microsoft AutoGen to perform real-time ethical and regulatory compliance analysis for financial market activities. Inspired by recent headlines regarding market integrity rules, this system will leverage a team of specialized agents to interpret complex market news, cross-reference legal frameworks (such as insider trading regulations), and flag potential violations. Qwen 3 235B will power the core reasoning capabilities for the agents, enabling nuanced understanding of verbose financial texts and intricate regulatory guidelines. The solution emphasizes modern AI interaction and evaluation. It should integrate seamlessly with Fixie for natural language voice interactions, allowing financial stakeholders to query compliance statuses and receive immediate alerts or summaries through conversational interfaces. Patronus AI will be employed for robust evaluation and monitoring of the agents' behavior, ensuring strict adherence to compliance policies, identifying any biases, or detecting errors in their decision-making processes. Furthermore, a Wix dashboard will be created to visualize compliance reports, agent decision-making rationales, and overall system performance, providing transparency and auditability.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
CorrectViolationIdentification
The system correctly identifies the primary compliance violation (e.g., 'Insider Trading') when present.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ReasoningClarity
The 'reasoning' provided by the agent system is clear, concise, and directly supports the compliance status.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
NoFalsePositives
The system does not flag violations for scenarios that are clearly compliant according to the rules.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Compliance Accuracy
The percentage of test cases where the 'compliance_status' matches the ground truth. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Reasoning Quality Score
A subjective score (1-5) based on the coherence, completeness, and correctness of the agent's reasoning. • target: 4 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Voice Response Latency (seconds)
The average time taken for the system to process a voice query via Fixie and return a spoken response. • target: 2 • range: 0-5
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 Microsoft AutoGen for orchestrating complex, conversational multi-agent workflows, defining agent roles and communication patterns.
Implement ethical guidelines and regulatory compliance checks within an AI agent system, focusing on financial market rules like insider trading prohibitions.
Integrate Qwen 3 235B as the backbone LLM for advanced text comprehension, legal document analysis, and sophisticated reasoning within agents.
Design voice-enabled interfaces using Fixie, allowing stakeholders to interact with the compliance system through natural language queries and receive spoken responses.
Utilize Patronus AI for robust evaluation and monitoring of agent behavior, ensuring compliance adherence, identifying potential biases, and tracing decision rationales.
Develop custom tool integrations for AutoGen agents, enabling them to access mock financial databases or legal rule engines for information retrieval.
Build a dynamic dashboard using Wix developer tools to visualize compliance reports, agent interactions, and audit trails.
How this agent runs
Evaluate the multi-agent system's ability to accurately identify compliance issues, its reasoning transparency, and its responsiveness via the Fixie voice interface.
Challenge input
JSON object with 'market_news' (string), 'trade_data' (JSON object with 'user', 'stock', 'action', 'volume', 'time'), and 'ethical_rules' (array of...
AutoGen
Agent Systems · Multi-Agent Systems
Patronus AI
Evaluation and guardrail platform.
Qwen 3
Policy Serving in the agent workflow.
Evaluated output
JSON object with 'compliance_status' (string), 'reasoning' (string), 'flagged_violations' (array of strings), and 'confidence' (float 0.0-1.0).
- The system correctly identifies the primary compliance violation (e.g., 'Insider Trading') when present.
- The 'reasoning' provided by the agent system is clear, concise, and directly supports the compliance status.
- The system does not flag violations for scenarios that are clearly compliant according to the rules.
- Compliance Accuracy target: 0.95
- 1 public reference case
- Python execution harness
View technical recipe
Configured tools
- AutoGen · Required
- Patronus AI · Optional
- Qwen 3 · Optional
Evaluation contract
- CorrectViolationIdentification · Weight 1
- ReasoningClarity · Weight 1
- NoFalsePositives · Weight 1
- Compliance Accuracy · Weight 1
- Reasoning Quality Score · Weight 1
- Voice Response Latency (seconds) · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.
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