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Challenge

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.

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Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Evaluates multi-agent consensus formation and correct ranking of gas field expansion contractors.

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

consensus_check

Verify agents reached explicit consensus

Binary check

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

Dimension 2

ranking_accuracy

Accuracy of frontrunner identification • target: 0.95 • range: 0.85-1

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

  • 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

Resources and assets

Reference links and supporting material

Dataset notes

Contractor technical proposals, fracking rig capabilities, and cost models for Saudi Jafurah field development.

Challenge setup

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

Evaluation checks
  • Verify agents reached explicit consensus
  • Accuracy of frontrunner identification • target: 0.95 • range: 0.85-1
Configured evaluation evidence
  • Benchmark: MultiAgentConsensusBench
  • Ranking Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • AutoGen · Required
  • Microsoft AutoGen · Optional
  • Zed · Optional

Evaluation contract

  • consensus_check · Weight 1
  • ranking_accuracy · Weight 1

Metrics

  • ranking_accuracy · Target 0.95

Benchmarks

  • MultiAgentConsensusBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing contractor profiles

    Output: JSON consensus ranking

    Execution harness: Python

Evaluation tools

  • AutoGen · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Gas Field Expansion Evaluator with AutoGen Multi-Agent Systems