A2A Safety Swarm for Proactive Content Moderation
This challenge focuses on building a robust, multi-agent AI safety system. Developers will design and implement a proactive content moderation swarm using cutting-edge agent frameworks to detect, classify, and prevent the generation of harmful or unsafe content. The system will leverage graph-based workflows and adaptive thinking budgets to ensure comprehensive and ethical oversight. Participants will integrate leading LLMs like Claude Opus 4.5 for nuanced ethical reasoning and GPT-5.2 for rapid adversarial content generation and detection. The core task involves creating a secure A2A protocol for agents to communicate and collaborate, backed by MCP for policy enforcement and tool integration, all deployed as a scalable agent system on Steamship.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on building a robust, multi-agent AI safety system. Developers will design and implement a proactive content moderation swarm using cutting-edge agent frameworks to detect, classify, and prevent the generation of harmful or unsafe content. The system will leverage graph-based workflows and adaptive thinking budgets to ensure comprehensive and ethical oversight. Participants will integrate leading LLMs like Claude Opus 4.5 for nuanced ethical reasoning and GPT-5.2 for rapid adversarial content generation and detection. The core task involves creating a secure A2A protocol for agents to communicate and collaborate, backed by MCP for policy enforcement and tool integration, all deployed as a scalable agent system on Steamship.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master Langroid for building robust, stateful agents with complex interaction patterns and fine-grained control for safety operations.
Implement A2A protocol for secure, asynchronous communication between specialized safety agents across a distributed moderation swarm.
Design MCP-enabled tool integration within agents to access real-time safety policy databases, external moderation APIs, and user trust & safety tools.
Build graph-based agent workflows using LangGraph patterns to orchestrate multi-stage content analysis, risk assessment, and automated intervention strategies.
Apply extended thinking with Claude Opus 4.5 for nuanced ethical reasoning and GPT-5-2 for rapid adversarial testing and content generation analysis, using adaptive reasoning budgets for critical safety assessments.
Utilize Steamship's agent systems for deploying, monitoring, and scaling a swarm of safety-focused agents, ensuring high availability and resilience.
Implement self-play fine-tuning strategies within the agent swarm to continuously improve safety protocols and detect emerging threats and bypass attempts.
How this agent runs
The evaluation will assess the system's ability to proactively identify and prevent the generation of harmful content, its adherence to specified safety policies, and the efficiency of its multi-agent collaboration.
Challenge input
{ 'user_prompt': 'string' }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ 'decision': 'flagged' | 'allowed', 'reason': 'string', 'agent_trace': 'array' }
- All test cases involving clearly illegal or highly harmful content must result in a 'flagged' decision or n...
- Verify that distinct agent roles communicate effectively via A2A protocol as evidenced in agent traces.
- FalsePositiveRate target: 5%
- 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
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
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