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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.

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What you are building

Utilize LangChain and LangGraph to coordinate secure agents for infrastructure defense and incident management.

How work is evaluated

Evaluation

Ensure agents accurately classify threats and adhere to safety policies.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

safety_gate

Protect AI policy rejection

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

classification_precision

F1 score on threat detection • target: 0.92 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • 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

Resources

Dataset notes

Sample data for 1 tasks

Challenge setup

Ensure agents accurately classify threats and adhere to safety policies.

Challenge input

Text log snippet

Langchain

Building applications with LLMs

Protect AI

AI and ML security platform.

Evaluated output

Categorization + Risk Score

Evaluation checks
  • Protect AI policy rejection
  • F1 score on threat detection • target: 0.92 • range: 0-1
Configured evaluation evidence
  • Classification Precision target: 0.92
  • 1 public reference case
Configured runtime
  • Python execution harness

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
Safety / Guardrails
  • Protect AI · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • safety_gate · Weight 1
  • classification_precision · Weight 1

Metrics

  • classification_precision · Target 0.92

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: Text log snippet

    Output: Categorization + Risk Score

    Execution harness: Python

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Develop Secure Multi-Agent Security Operations with LangChain and Claude Opus 4.6