Develop Secure Multi-Agent Security Operations with LangChain and Claude Opus 4.6
Create a multi-agent defense swarm to monitor and categorize potential cybersecurity incidents in critical infrastructure. Using LangChain and LangGraph, you will design a hierarchical team where specialists assess vulnerabilities and manage defensive responses. This challenge focuses on secure agent practices using Protect AI to guard against model prompt injection and systemic risks. The system utilizes All Hands AI to facilitate human-in-the-loop interactions for complex edge cases. Mem0 is integrated to maintain long-term memory of specific infrastructure configurations and past threat profiles, enabling the agents to evolve their defensive posture without relying on static databases.
What you are building
The core problem, expected build, and operating context for this challenge.
Create a multi-agent defense swarm to monitor and categorize potential cybersecurity incidents in critical infrastructure. Using LangChain and LangGraph, you will design a hierarchical team where specialists assess vulnerabilities and manage defensive responses. This challenge focuses on secure agent practices using Protect AI to guard against model prompt injection and systemic risks. The system utilizes All Hands AI to facilitate human-in-the-loop interactions for complex edge cases. Mem0 is integrated to maintain long-term memory of specific infrastructure configurations and past threat profiles, enabling the agents to evolve their defensive posture without relying on static databases.
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, how much each dimension matters, and which criteria separate a passable run from a strong one.
safety_gate
Protect AI policy rejection
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
classification_precision
F1 score on threat detection • target: 0.92 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Orchestrate hierarchical agent teams using LangGraph to separate threat analysis and response logic
Design agentic memory structures using Mem0 for context persistence across long-running security sessions
Implement AI-native security protocols with Protect AI to audit agent-model communications
Connect All Hands AI for real-time developer intervention in critical threat scenarios
Utilize Claude Opus 4.6 for sophisticated policy enforcement and regulatory analysis
Build modular state machines for autonomous decision-making in high-risk environments
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[ok] Wrote eval/examples.json
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