Orchestrate a CrewAI Industrial Feasibility Squad with Pydantic AI for GCC Green Iron Projects
Oman's Meranti green iron project and Saudi Arabia's push to localize desalination production require rigorous feasibility analysis. This challenge tasks you with building a multi-agent system using CrewAI to evaluate the economic and technical viability of these industrial projects. You will create three specialized agents: a 'Regional Compliance Expert', a 'Supply Chain Analyst', and a 'Sustainability Consultant'. Each agent will utilize Pydantic AI for structured data validation, ensuring that all findings (e.g., water requirements, carbon footprint, local content spend) adhere to strict data schemas. The agents must collaborate to produce a final 'Go/No-Go' report that accounts for Oman's water supply constraints from Marafiq and Saudi's localization targets. The goal is to move from manual PDF-based feasibility studies to an automated, structured agentic analysis pipeline.
What you are building
The core problem, expected build, and operating context for this challenge.
Oman's Meranti green iron project and Saudi Arabia's push to localize desalination production require rigorous feasibility analysis. This challenge tasks you with building a multi-agent system using CrewAI to evaluate the economic and technical viability of these industrial projects. You will create three specialized agents: a 'Regional Compliance Expert', a 'Supply Chain Analyst', and a 'Sustainability Consultant'. Each agent will utilize Pydantic AI for structured data validation, ensuring that all findings (e.g., water requirements, carbon footprint, local content spend) adhere to strict data schemas. The agents must collaborate to produce a final 'Go/No-Go' report that accounts for Oman's water supply constraints from Marafiq and Saudi's localization targets. The goal is to move from manual PDF-based feasibility studies to an automated, structured agentic analysis pipeline.
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.
Schema Validation
Validates that the output exactly matches the Pydantic FeasibilityModel.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Collaborative Synthesis
Quality score for how well agents integrated each other's data • target: 4.5 • range: 1-5
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 Agent and Task definitions to model professional engineering roles
Integrate Pydantic AI schemas to enforce data integrity during multi-agent handoffs
Build custom CrewAI Tools for searching regional industrial news (MEED/Zawya)
Design a collaborative 'Manager Agent' that synthesizes conflicting reports from specialists
Implement a cost-benefit model for 'Local Content' vs 'Imported Equipment' using agent reasoning
Orchestrate a final report generation phase that produces structured JSON and formatted PDF output
[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.