implementation

Langroid Agent Implementation

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: A2A Safety Swarm for Proactive Content Moderation

Format

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Linked challenge

A2A Safety Swarm for Proactive Content Moderation

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Implement the core Langroid agents identified in your architecture. Focus on developing the 'Prompt Analyzer' and 'Policy Checker' agents, ensuring they can process user input, query an external (mock) policy database using MCP-enabled tool integration, and communicate their findings via A2A protocol. Incorporate adaptive thinking budgets for the Policy Checker when dealing with ambiguous prompts.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

Prompt diagnostics

Variables

Lists

Code blocks

Purpose

implementation

This prompt is mostly narrative and instruction-driven, so adapt examples and output constraints before you rewrite the structure.

Linked challenge

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.

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