Skip to main content
Loading navigation
Back to challenges

Green Hydrogen Feasibility Assessment Crew with CrewAI and Amazon Bedrock

Volatile renewable energy pricing and off-taker commitment shifts require rapid re-evaluation of green hydrogen project economics. Deploy a CrewAI multi-agent team running on Amazon Bedrock to evaluate electrolyzer capacity, solar tariffs, and off-take terms, outputting dynamic IRR models that accurately flag unviable project conditions.

Business OperationsHosted by Vera
Sign in free to start

What you are building

Build an autonomous multi-agent economic analysis team using CrewAI and Amazon Bedrock to evaluate green hydrogen megaproject feasibility.

How work is evaluated

Evaluation

Evaluates LCOH calculations and feasibility recommendations generated by the crew.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

correct_shelve_decision

Agent crew must recommend SHELVE when LCOH exceeds off-take cap

Binary check

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

Dimension 2

lcoh_calculation_accuracy

Accuracy of financial LCOH estimation relative to standard benchmark formula • target: 0.95 • range: 0-1

Binary check

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

Learning goals

  • Define specialized CrewAI agents (Solar Analyst, Electrolyzer Specialist, Financial Modeler)

  • Configure Amazon Bedrock API provider using Anthropic Claude models

  • Implement sequential agent delegation task workflows for project IRR calculations

  • Identify tipping points where hydrogen production costs exceed market off-taker caps

Resources

Dataset notes

Masdar Abu Dhabi green hydrogen project parameters, electrolyzer cost curves, and solar PPA pricing models.

Challenge setup

Evaluates LCOH calculations and feasibility recommendations generated by the crew.

Challenge input

JSON containing solar PPA tariff ($/MWh), electrolyzer cost ($/kW), and off-take cap ($/kg).

CrewAI

Required multi-agent role-playing framework

Amazon Bedrock

Required cloud model runtime environment

Evaluated output

JSON containing estimated LCOH ($/kg), project IRR %, and recommendation ('PROCEED' or 'SHELVE').

Evaluation checks
  • Agent crew must recommend SHELVE when LCOH exceeds off-take cap
  • Accuracy of financial LCOH estimation relative to standard benchmark formula • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Renewable Energy Economic Analysis Benchmark
  • Lcoh Calculation Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • CrewAI · Required
  • crewAI · Optional
  • Amazon Bedrock · Optional

Evaluation contract

  • correct_shelve_decision · Weight 1
  • lcoh_calculation_accuracy · Weight 1

Metrics

  • lcoh_calculation_accuracy · Target 0.95

Benchmarks

  • Renewable Energy Economic Analysis Benchmark
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing solar PPA tariff ($/MWh), electrolyzer cost ($/kW), and off-take cap ($/kg).

    Output: JSON containing estimated LCOH ($/kg), project IRR %, and recommendation ('PROCEED' or 'SHELVE').

    Execution harness: Python

Evaluation tools

  • CrewAI · Agent Framework
  • Amazon Bedrock · Inference Infrastructure

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Green Hydrogen Feasibility Assessment Crew with CrewAI and Amazon Bedrock