Green Hydrogen Feasibility Multi-Agent System with AutoGen
Masdar's strategic decision to re-evaluate green hydrogen projects in Abu Dhabi requires multi-faceted analysis of levelized cost, grid power tariffs, and off-taker demand. Build a multi-agent simulation system using AutoGen and Groq Cloud to simulate trade-off negotiations between finance, engineering, and regulatory agents. Achieve multi-agent consensus in under 10 conversation turns.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement an AutoGen conversational multi-agent system powered by Groq Cloud ultra-fast inference to run project economic feasibility simulations.
How work is evaluated
Evaluates convergence speed and financial accuracy of AutoGen multi-agent feasibility deliberations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
consensus_achieved
Checks if group chat ended with explicit consensus marker
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
conversation_turns_count
Number of message turns required to reach stable consensus • target: 8 • range: 1-20
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Configure AutoGen multi-agent group chats with specialized domain personas
Utilize Groq Cloud high-throughput inference for low-latency multi-agent debates
Model levelized cost of hydrogen (LCOH) against fluctuating electrolyzer and power prices
Synthesize clear project termination vs restructuring recommendations
Reference links and supporting material
Economic scenario matrix of 25 green hydrogen projects with varied power tariffs, CAPEX, and regulatory subsidies.
Challenge setup
Evaluates convergence speed and financial accuracy of AutoGen multi-agent feasibility deliberations.
Challenge input
JSON with power price ($/MWh), CAPEX ($/kW), and off-taker buyer target price
AutoGen
Enables multi-persona conversational workflows with automated group chat management.
Groq Cloud
Provides ultra-fast token generation essential for multi-turn agent discussions.
Evaluated output
JSON containing decision (PROCEED/SHELVING_RECOMMENDED), calculated LCOH, and consensus turns
- Checks if group chat ended with explicit consensus marker
- Number of message turns required to reach stable consensus • target: 8 • range: 1-20
- Benchmark: MultiAgentConsensusBench
- Conversation Turns Count target: 8
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- AutoGen · Required
- Zed · Optional
- Groq Cloud · Optional
Evaluation contract
- consensus_achieved · Weight 1
- conversation_turns_count · Weight 1
Metrics
- conversation_turns_count · Target 8
Benchmarks
- MultiAgentConsensusBench
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with power price ($/MWh), CAPEX ($/kW), and off-taker buyer target price
Output: JSON containing decision (PROCEED/SHELVING_RECOMMENDED), calculated LCOH, and consensus turns
Execution harness: Python
Evaluation tools
- AutoGen · Multi-Agent Framework
- Groq Cloud · Inference Acceleration
Recipe freeze marker
No recipe freeze time is recorded.