Gigaproject Leadership & Schedule Governance Multi-Agent Simulator in AutoGen
Leadership transitions in Saudi Arabia real estate gigaprojects often trigger project schedule reviews. Build an AutoGen multi-agent conversational simulator featuring Developer, Contractor, and Governance agents that negotiate project timeline adjustments and output an updated milestone roadmap.
What you are building
The core problem, expected build, and operating context for this challenge.
Utilize Microsoft AutoGen multi-agent conversation frameworks to simulate real estate gigaproject executive governance and schedule renegotiations.
How work is evaluated
Evaluates AutoGen agents' ability to reach consensus on milestone extensions under leadership replacement scenarios.
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 State Test
Ensures conversational agents successfully reach consensus.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Negotiation Variance
Difference between proposed extension and compromise deadline. • target: 4 • range: 0-12
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 Microsoft AutoGen ConversationalAgent patterns for corporate governance
Simulate executive negotiation between project CEO, EPC contractors, and auditors
Re-evaluate gigaproject delivery milestones following executive replacements
Generate structured milestone revision summaries with human-in-the-loop review steps
Reference links and supporting material
Synthetic leadership transition records and master schedule delay metrics for Saudi Arabia real estate gigaprojects.
How this agent runs
Evaluates AutoGen agents' ability to reach consensus on milestone extensions under leadership replacement scenarios.
Challenge input
JSON object with original_deadline, requested_extension_months, and chief_executive_status.
AutoGen
Multi-agent conversational negotiation and governance simulation
Evaluated output
JSON object with agreed_extension_months, consensus_reached, and revised_deadline.
- Ensures conversational agents successfully reach consensus.
- Difference between proposed extension and compromise deadline. • target: 4 • range: 0-12
- Benchmark: GCC Real Estate Governance Benchmark
- Negotiation Variance target: 4
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- AutoGen · Required
- Synthflow · Optional
- OpenRouter · Optional
- OpenRouter · Optional
Evaluation contract
- Consensus State Test · Weight 1
- Negotiation Variance · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.