Build an Industrial Smelter Restoration Multi-Agent Swarm with AutoGen
Complex industrial plant restorations require synchronized structural, electrical, and operational risk assessment. Build a multi-agent orchestration system using Microsoft AutoGen and Vercel AI SDK to simulate expert consensus and schedule restoration tasks, delivering a validated project plan with zero resource allocation deadlocks.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate structural, environmental, and electrical specialist agents using AutoGen and Vercel AI SDK runtime.
How work is evaluated
Tests AutoGen multi-agent consensus generation on Al-Taweelah smelter restoration schedule data.
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.
zero_conflicts_check
Checks that unresolved_conflicts equals 0
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
schedule_efficiency
Ratio of optimal schedule duration vs generated schedule duration • target: 0.95 • range: 0-1
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 expert roles (Structural, Metallurgical, EPC Scheduler) in Microsoft AutoGen
Use Vercel AI SDK to stream multi-agent conversation state and UI status components
Implement group chat managers to reach automated consensus on potline restoration milestones
Enforce safety constraint verification before outputting construction sequence plans
Reference links and supporting material
Simulated industrial damage assessments and repair labor datasets based on EGA Al-Taweelah restoration milestones.
How this agent runs
Tests AutoGen multi-agent consensus generation on Al-Taweelah smelter restoration schedule data.
Challenge input
JSON containing damage_assessment, available_crew_shifts, target_completion_weeks
AutoGen
Multi-agent conversational consensus
AI SDK
Real-time state streaming to UI
Evaluated output
JSON containing scheduled_tasks, critical_path_length_days, unresolved_conflicts
- Checks that unresolved_conflicts equals 0
- Ratio of optimal schedule duration vs generated schedule duration • target: 0.95 • range: 0-1
- Benchmark: Multi-Agent Planning Benchmark
- Schedule Efficiency target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- AutoGen · Required
- Vercel AI SDK · Optional
- Zed · Optional
- Vercel AI SDK · Optional
Evaluation contract
- zero_conflicts_check · Weight 1
- schedule_efficiency · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.