Red-Team AI Safety with GPT-5 and LangGraph Adaptive Thinking Agents
Inspired by recent concerns regarding AI's potential for large-scale destruction, this challenge tasks developers with building a sophisticated multi-agent red-teaming system. The system will simulate a 'red team' of adversarial agents and a 'blue team' of defensive agents, using advanced generative AI to identify and mitigate potential vulnerabilities in hypothetical AI systems or their societal deployments. This involves simulating complex scenarios, identifying emergent risks, and proposing robust safeguards. Participants will leverage GPT-5's advanced reasoning capabilities for scenario generation and risk assessment, orchestrated within a LangGraph-powered graph-based workflow. The core innovation lies in implementing extended thinking patterns with adaptive reasoning budgets, allowing agents to dynamically adjust their cognitive resources based on the complexity and novelty of encountered threats. Agent-to-agent (A2A) communication will be crucial for adversarial interaction and collaborative defense strategies.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an advanced multi-agent red-teaming system using GPT-5 and LangGraph, implementing extended thinking with adaptive reasoning budgets and A2A communication to simulate AI safety scenarios.
How work is evaluated
The system will be evaluated on its ability to effectively simulate AI safety red-teaming, generate plausible threat scenarios, identify vulnerabilities, and propose concrete mitigation strategies, focusing on the sophistication of agent interactions and adaptive reasoning.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Design and implement graph-based agent workflows using LangGraph for complex adversarial simulations.
Develop extended thinking patterns with adaptive reasoning budgets for efficient problem-solving under varying complexity.
Integrate GPT-5 for advanced scenario generation, threat identification, and countermeasure development.
Build A2A protocol agents to enable secure and structured communication between red and blue teams.
Reference links and supporting material
Sample data for 2 tasks
Challenge setup
The system will be evaluated on its ability to effectively simulate AI safety red-teaming, generate plausible threat scenarios, identify vulnerabilities, and propose concrete mitigation strategies, focusing on the sop...
Challenge input
{'ai_system_description': 'string', 'contextual_parameters': ['string']}
Agent execution
No agent tool is listed for this challenge.
Evaluated output
{'scenario_title': 'string', 'threat_vector': 'string', 'catastrophic_risk_score': 'float', 'identified_vulnerabilities': ['string'], 'reasoning_pa...
- Red Team's generated scenario must be logically consistent and technically plausible.
- Blue Team's mitigation plan must address all identified vulnerabilities and include at least 2 high-priorit...
- Verify that A2A messages were properly formatted and exchanged between agents during the simulation (requir...
- ScenarioNoveltyScore target: 0.8
- 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.
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Configured tools
No tool records are attached.
Evaluation contract
- Red Team's generated scenario must be logically consistent and technically plausible.
- Blue Team's mitigation plan must address all identified vulnerabilities and include at least 2 high-priority actions.
- Verify that A2A messages were properly formatted and exchanged between agents during the simulation (requires logging analysis).
Metrics
- ScenarioNoveltyScore · Target 0.8
- AdaptiveBudgetEfficiency · Target 0.75
- MitigationEffectiveness · Target 0.85
Workflow tasks
- Task 1
Input: {'ai_system_description': 'string', 'contextual_parameters': ['string']}
Output: {'scenario_title': 'string', 'threat_vector': 'string', 'catastrophic_risk_score': 'float', 'identified_vulnerabilities': ['string'], 'reasoning_path': ['string']}
Execution harness: Python
- Task 2
Input: {'threat_scenario': {'scenario_title': 'string', 'threat_vector': 'string', 'identified_vulnerabilities': ['string']}, 'budget_constraints': {'time_days': 'int', 'resources_usd': 'int'}}
Output: {'mitigation_plan_summary': 'string', 'detailed_actions': [{'action': 'string', 'priority': 'string', 'estimated_cost_usd': 'int', 'effectiveness_score': 'float'}], 'adaptive_thinking_usage': 'dict'}
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.