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Challenge

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.

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Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Tests AutoGen multi-agent consensus generation on Al-Taweelah smelter restoration schedule data.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

zero_conflicts_check

Checks that unresolved_conflicts equals 0

Binary check

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

Dimension 2

schedule_efficiency

Ratio of optimal schedule duration vs generated schedule duration • target: 0.95 • range: 0-1

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 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

Resources and assets

Reference links and supporting material

Dataset notes

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 for
  • Checks that unresolved_conflicts equals 0
  • Ratio of optimal schedule duration vs generated schedule duration • target: 0.95 • range: 0-1
Proof of success
  • Benchmark: Multi-Agent Planning Benchmark
  • Schedule Efficiency target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • AutoGen · Required
  • Vercel AI SDK · Optional
  • Zed · Optional
Orchestration
  • 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.

Frequently Asked Questions about Build an Industrial Smelter Restoration Multi-Agent Swarm with AutoGen