planning

Define CrewAI Agent Roles and A2A Workflow

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

Linked challenge: Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP

Format

Text-first

Lines

Sections

Linked challenge

Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP

Prompt source

Original prompt text with formatting preserved for inspection.

1 lines

1 sections

No variables

0 checklist items

Design the CrewAI multi-agent team. Define at least three distinct agent roles (e.g., 'Content Analyzer', 'Ethical Compliance Agent', 'Report Synthesizer') with specific goals and backstories. Detail the A2A communication flow between them, outlining how tasks are delegated, information is shared, and collaborative decisions are made to evaluate AI-generated content. How will adaptive thinking budgets be applied?

Adaptation plan

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

Keep stable

Preserve the role framing, objective, and reporting structure so comparison runs stay coherent.

Tune next

Swap in your own domain constraints, anomaly thresholds, and examples before you branch variants.

Verify after

Check whether the prompt asks for the right evidence, confidence signal, and escalation path.

Prompt diagnostics

Variables

Lists

Code blocks

Purpose

planning

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

Linked challenge

Orchestrate a GPT-5 & Cohere R+ Quality Assurance Crew with DSPy & MCP

Following the backlash against AI-generated dubs, this challenge tasks you with building a sophisticated multi-agent system to ensure the quality and cultural appropriateness of AI-generated content (e.g., text, audio transcriptions). You will orchestrate a CrewAI team, utilizing GPT-5 for advanced critical analysis and Cohere Command R+ for robust RAG over style guides and cultural sensitivity documents. The system must detect subtle errors, cultural nuances, and potential 'hallucinations' that could lead to poor user experiences. This project emphasizes agent-to-agent (A2A) communication, adaptive thinking budgets, and MCP tool integration for a human-in-the-loop validation workflow. You will also employ DSPy to programmatically optimize agent prompts for higher evaluation accuracy, ensuring that AI-generated content meets stringent quality standards before deployment. The goal is to create a resilient and ethically aware content QA pipeline.

Open challenge

Related prompts

Browse library

Implement DSPy for Prompt Optimization

implementation

Integrate MCP Tools for Human-in-the-Loop Feedback

implementation

Craft GPT-5 & Cohere R+ Prompts for Advanced Analysis

implementation