Challenge

Strategic Market Intel

In light of the intense competition among AI giants like OpenAI and Anthropic, this challenge focuses on building a dynamic competitive intelligence system. Participants will use Mastra AI to orchestrate a multi-agent team that gathers, analyzes, and synthesizes market data to provide strategic insights. The system will leverage Claude 4 Opus for sophisticated analysis, Gemini 3 Flash for rapid data processing, and integrate with various data sources using Lyzr, while utilizing Upstage for specialized document understanding. The goal is to generate actionable strategic recommendations for a hypothetical AI startup.

Workflow AutomationHosted by Vera
Status
Always open
Difficulty
Advanced
Points
500
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

In light of the intense competition among AI giants like OpenAI and Anthropic, this challenge focuses on building a dynamic competitive intelligence system. Participants will use Mastra AI to orchestrate a multi-agent team that gathers, analyzes, and synthesizes market data to provide strategic insights. The system will leverage Claude 4 Opus for sophisticated analysis, Gemini 3 Flash for rapid data processing, and integrate with various data sources using Lyzr, while utilizing Upstage for specialized document understanding. The goal is to generate actionable strategic recommendations for a hypothetical AI startup.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

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.

Max Score: 4
Dimensions
4 scoring checks
Binary
4 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1report_structure_adherence

Report Structure Adherence

The generated report must contain all specified sections in a logical order.

binary
Weight: 1
Binary check

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

Dimension 2agent_workflow_completion

Agent Workflow Completion

All defined agents in the Mastra AI workflow must complete their tasks without critical errors.

binary
Weight: 1
Binary check

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

Dimension 3strategic_insight_score

Strategic Insight Score

Expert evaluation of the report's depth, originality, and actionability of recommendations (0-100). • target: 80 • range: 0-100

binary
Weight: 1
Binary check

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

Dimension 4data_extraction_accuracy

Data Extraction Accuracy

F1-score for entity extraction from unstructured text (0-1). • target: 0.9 • range: 0-1

binary
Weight: 1
Binary check

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

Learning goals

What you should walk away with

  • Master Mastra AI's agent definition, memory management, and workflow orchestration capabilities to build a robust competitive intelligence system.

  • Implement agents with Claude 4 Opus to conduct deep strategic analysis, synthesize complex market trends, and formulate actionable business recommendations.

  • Integrate Gemini 3 Flash for high-speed processing of large volumes of market data, including summarizing news articles, financial reports, and social media feeds.

  • Design specialized agents that utilize Upstage's document AI capabilities to extract structured information from unstructured text, such as competitor press releases or research papers.

  • Build data ingestion tools using Lyzr's low-code integration platform to connect Mastra AI agents with various external APIs and web data sources (e.g., news APIs, financial data services).

  • Develop A2A communication protocols within Mastra AI to enable seamless collaboration between 'Market Researcher', 'Data Analyst', and 'Strategy Formulator' agents.

  • Explore patterns for deploying and managing Mastra AI agents in a production environment, considering aspects often handled by platforms like Sema4.ai (e.g., scalability, monitoring).

Start from your terminal
$npx -y @versalist/cli start strategic-market-intel

[ok] Wrote CHALLENGE.md

[ok] Wrote .versalist.json

[ok] Wrote eval/examples.json

Requires VERSALIST_API_KEY. Works with any MCP-aware editor.

Docs
Manage API keys
Host and timing
Vera

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Your progress

Participation status

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Timeline and host

Operating window

Key dates and the organization behind this challenge.

Start date
Available now
Run mode
Evergreen challenge
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Useful when you want to pressure-test your workflow on a new dataset, new constraints, or a new evaluation rubric.

Tool Space Recipe

Draft
Action Space
Mastra AIAI agents for sales engagement
required
LyzrEnterprise AI agent builder
Policy Serving
Claude 4 Opus
Evaluation
Rubric: 4 dimensions
·Report Structure Adherence(1%)
·Agent Workflow Completion(1%)
·Strategic Insight Score(1%)
·Data Extraction Accuracy(1%)
Gold items: 2 (2 public)

Frequently Asked Questions about Strategic Market Intel