Build MCP-Enabled Social Media Policy Enforcement Agents
Companies are increasing social media monitoring due to reputational risks from employee posts. This challenge involves building an advanced multi-agent system that autonomously monitors public social media feeds for potential employee policy violations. The system will leverage a graph-based workflow to analyze content, interpret company policies (retrieved via RAG), and flag potential issues, while adapting its reasoning budget based on the sensitivity of the content or the severity of the potential violation. You will design and implement a sophisticated LangGraph-based agent network where a 'Monitor Agent' feeds data to a 'Policy Interpretation Agent' and a 'Risk Assessment Agent'. These agents will communicate using a defined protocol, integrating with external enterprise policy databases via MCP for real-time policy lookup and dynamically adjusting their processing depth (thinking budget) to balance efficiency and accuracy. The system must provide actionable insights and prioritize alerts for human review, ensuring compliance while minimizing false positives.
What you are building
The core problem, expected build, and operating context for this challenge.
Companies are increasing social media monitoring due to reputational risks from employee posts. This challenge involves building an advanced multi-agent system that autonomously monitors public social media feeds for potential employee policy violations. The system will leverage a graph-based workflow to analyze content, interpret company policies (retrieved via RAG), and flag potential issues, while adapting its reasoning budget based on the sensitivity of the content or the severity of the potential violation. You will design and implement a sophisticated LangGraph-based agent network where a 'Monitor Agent' feeds data to a 'Policy Interpretation Agent' and a 'Risk Assessment Agent'. These agents will communicate using a defined protocol, integrating with external enterprise policy databases via MCP for real-time policy lookup and dynamically adjusting their processing depth (thinking budget) to balance efficiency and accuracy. The system must provide actionable insights and prioritize alerts for human review, ensuring compliance while minimizing false positives.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building complex, stateful Directed Acyclic Graph (DAG) workflows for multi-agent systems, including node and edge definitions, and state management.
Implement MCP-enabled tool integration using Claude Opus 4.5 to securely connect agents to simulated enterprise HR policy databases and social media APIs.
Design and deploy 'Monitor', 'Policy Interpretation', and 'Risk Assessment' agents using Claude Opus 4.1 and Mistral Nemo, defining their roles, responsibilities, and communication protocols.
Build extended thinking pipelines where agents dynamically adjust their reasoning budget based on the input's complexity or the detected risk level, optimizing for both speed and accuracy.
Integrate LlamaIndex for robust RAG over diverse policy documents, allowing agents to retrieve and synthesize relevant policy clauses in real-time for nuanced interpretation.
Orchestrate the flow of information and decisions across agents within LangGraph, ensuring effective handoffs and collaborative problem-solving for policy compliance.
How this agent runs
The evaluation will assess the system's ability to accurately identify policy violations, its adaptability in reasoning, and the efficiency of its agent communication within the LangGraph framework. Emphasis will be p...
Challenge input
{ 'social_media_posts': [{'id': 'str', 'text': 'str', 'author_id': 'str'}], 'company_policies': [{'id': 'str', 'text': 'str'}] }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ 'alerts': [{'post_id': 'str', 'violation_type': 'str', 'reasoning': 'str', 'severity': 'low|medium|high', 'adaptive_budget_used': 'float'}] }
- Successfully identifies all planted policy violations in the sample input.
- Demonstrates successful MCP calls for policy lookup in the agent's internal logs.
- Shows evidence of dynamic reasoning budget adjustment based on detected severity.
- Precision target: 0.95
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
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Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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