Agentic AI Chatbot Safety & Compliance with Gemini 2.5 Pro and CrewAI
Design and implement a cutting-edge agentic AI system inspired by California's new SB 243 law, which mandates safety protocols for AI chatbot companions. This challenge focuses on building a multi-agent system capable of proactively monitoring, identifying, and mitigating unsafe or non-compliant interactions in real-time. The system will leverage advanced LLMs for nuanced conversational understanding and ethical reasoning, orchestrating agents to ensure adherence to regulatory guidelines. Participants will construct a CrewAI-based team of specialized agents—a 'Conversation Monitor,' an 'Ethical Auditor,' and a 'Compliance Reporter'—that communicate via a robust A2A protocol. The system must integrate RAG capabilities for accessing regulatory documents and employ extended thinking with adaptive reasoning budgets to handle complex, ambiguous safety scenarios, generating detailed compliance logs and alerts for review.
What you are building
The core problem, expected build, and operating context for this challenge.
Design and implement a cutting-edge agentic AI system inspired by California's new SB 243 law, which mandates safety protocols for AI chatbot companions. This challenge focuses on building a multi-agent system capable of proactively monitoring, identifying, and mitigating unsafe or non-compliant interactions in real-time. The system will leverage advanced LLMs for nuanced conversational understanding and ethical reasoning, orchestrating agents to ensure adherence to regulatory guidelines. Participants will construct a CrewAI-based team of specialized agents—a 'Conversation Monitor,' an 'Ethical Auditor,' and a 'Compliance Reporter'—that communicate via a robust A2A protocol. The system must integrate RAG capabilities for accessing regulatory documents and employ extended thinking with adaptive reasoning budgets to handle complex, ambiguous safety scenarios, generating detailed compliance logs and alerts for review.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master CrewAI for orchestrating role-based agent teams with specialized skills and clear objectives for compliance.
Implement A2A protocol for secure, structured agent-to-agent communication, ensuring data integrity and auditability.
Deploy Gemini 2.5 Pro with Deep Think mode for advanced ethical reasoning, identifying nuanced safety violations and potential harms.
Integrate RAG capabilities to provide agents with real-time access to California SB 243 and other relevant safety regulations.
Build extended thinking pipelines using adaptive reasoning budgets to allow agents to perform deep analysis on flagged conversations without excessive token usage.
Design and implement tool integration via a lightweight MCP-like pattern for logging, alerting, and external data access within the safety system.
How this agent runs
The evaluation will assess the system's ability to accurately identify safety violations, generate appropriate mitigation responses, and produce comprehensive compliance reports, with a focus on agent collaboration an...
Challenge input
JSON array of chat turn objects: [{'speaker': 'user'|'chatbot', 'text': '...'}]
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
JSON object: {'violations_detected': ['list of violation types'], 'flagged_segment': 'string', 'reasoning': 'string'}
- Verify all CrewAI agents successfully communicate and complete their tasks within the workflow.
- Check if relevant regulatory document excerpts are cited in compliance reports when applicable.
- Confirm that the system intelligently applies extended thinking only for flagged complex scenarios.
- ViolationDetectionAccuracy target: 0.9
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
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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