Build a Gas Field Expansion Evaluator with AutoGen Multi-Agent Systems
Evaluating complex field expansion bids for Saudi Arabia's Jafurah gas project requires coordinated specialized expertise. Build a multi-agent conversational system using Microsoft AutoGen to simulate joint evaluations between drilling, environmental, and financial analyst agents.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an AutoGen multi-agent debate and consensus pipeline to assess bidder capability for the Jafurah gas expansion project.
How work is evaluated
Evaluates multi-agent consensus formation and correct ranking of gas field expansion contractors.
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 and which criteria separate a passable run from a strong one.
consensus_check
Verify agents reached explicit consensus
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
ranking_accuracy
Accuracy of frontrunner identification • target: 0.95 • range: 0.85-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Configure AutoGen UserProxyAgent and AssistantAgent instances with domain prompts
Implement group chat orchestrators to mediate consensus between conflicting bidder ratings
Evaluate unconventional gas drilling capabilities and environmental footprint claims
Output consolidated bidding rank matrices with transparent reasoning traces
Reference links and supporting material
Contractor technical proposals, fracking rig capabilities, and cost models for Saudi Jafurah field development.
How this agent runs
Evaluates multi-agent consensus formation and correct ranking of gas field expansion contractors.
Challenge input
JSON containing contractor profiles
AutoGen
Microsoft framework built specifically for multi-agent conversational patterns
Evaluated output
JSON consensus ranking
- Verify agents reached explicit consensus
- Accuracy of frontrunner identification • target: 0.95 • range: 0.85-1
- Benchmark: MultiAgentConsensusBench
- Ranking Accuracy target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- AutoGen · Required
- Microsoft AutoGen · Optional
- Zed · Optional
Evaluation contract
- consensus_check · Weight 1
- ranking_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.