Agents for Prompt-Driven Brand Sentiment & Affinity
This challenge focuses on building a sophisticated multi-agent system using CrewAI to analyze brand mentions and sentiment within a stream of AI-generated prompts. The system will orchestrate specialized agents to monitor, categorize, and report on brand affinity. The core idea is to simulate an intelligent monitoring platform that provides actionable insights into brand perception and recommendation patterns from diverse data sources, leveraging CrewAI's role-playing architecture. Developers will design a collaborative agent team, where each agent has a distinct role, tools, and goals. For instance, a 'Prompt Ingestor' agent, a 'Brand Analyst' agent, and a 'Report Generator' agent will work in concert. The system will use DeepSeek R1 for its advanced reasoning capabilities to accurately interpret nuanced sentiment and complex brand associations. Integration with Zapier Interfaces will enable seamless data ingestion from various sources, while tool can facilitate custom workflow automation for alert triggers and data processing. Voiceflow will provide a conversational interface for real-time query and status updates, making the system highly interactive. Blaxel will serve as the underlying agent orchestration backbone, ensuring robust agent management.
What you are building
The core problem, expected build, and operating context for this challenge.
This challenge focuses on building a sophisticated multi-agent system using CrewAI to analyze brand mentions and sentiment within a stream of AI-generated prompts. The system will orchestrate specialized agents to monitor, categorize, and report on brand affinity. The core idea is to simulate an intelligent monitoring platform that provides actionable insights into brand perception and recommendation patterns from diverse data sources, leveraging CrewAI's role-playing architecture. Developers will design a collaborative agent team, where each agent has a distinct role, tools, and goals. For instance, a 'Prompt Ingestor' agent, a 'Brand Analyst' agent, and a 'Report Generator' agent will work in concert. The system will use DeepSeek R1 for its advanced reasoning capabilities to accurately interpret nuanced sentiment and complex brand associations. Integration with Zapier Interfaces will enable seamless data ingestion from various sources, while tool can facilitate custom workflow automation for alert triggers and data processing. Voiceflow will provide a conversational interface for real-time query and status updates, making the system highly interactive. Blaxel will serve as the underlying agent orchestration backbone, ensuring robust agent management.
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.
SentimentAccuracy
At least 85% accuracy in identifying sentiment for given brand mentions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
BrandExtractionCompleteness
Identifies all relevant brand mentions (80% recall).
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
KoreAiIntegration
Kore.ai workflow is successfully triggered on specified conditions.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
VoiceflowResponsiveness
Voiceflow agent provides coherent and relevant responses to queries.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
AverageSentimentAccuracy
Average accuracy of sentiment classification across all extracted brands. • target: 0.9 • range: 0.75-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ToolUsageEffectiveness
Percentage of tasks where tools were correctly invoked and utilized. • target: 0.95 • range: 0.8-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Master CrewAI for building robust, role-based agent teams with defined responsibilities and collaborative workflows for data analysis.
Implement advanced sentiment analysis and entity extraction using DeepSeek R1's API for nuanced understanding of brand mentions in diverse prompts.
Design and build custom tools for CrewAI agents to programmatically interact with Zapier Interfaces for data ingestion and event triggering.
Integrate Kore.ai to define and execute complex workflow automations based on agent-detected insights and threshold breaches.
Develop a Voiceflow project to create an intuitive voice-enabled or chat-based interface for querying system status and brand analytics.
Leverage Blaxel's capabilities for monitoring agent interactions, managing agent lifecycles, and ensuring system resilience in production environments.
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
Requires VERSALIST_API_KEY. Works with any MCP-aware editor.
DocsAI Research & Mentorship
Participation status
You haven't started this challenge yet
Operating window
Key dates and the organization behind this challenge.
Find another challenge
Jump to a random challenge when you want a fresh benchmark or a different problem space.