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Challenge

Distributed Inference Fault Injection & Recovery Simulator with AutoGen

Orchestrate a multi-agent distributed inference fault simulator using AutoGen to test node failover and KV-cache recovery protocols during high-throughput serving. Maintain zero dropped user requests and recover lost state in under 500 milliseconds during simulated hardware outages.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a multi-agent cluster fault injection and recovery simulation framework using AutoGen.

Delivery guide

How work is evaluated

Evaluation

Evaluates dropped request rate and state recovery duration during hardware fault injection.

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

Zero Dropped Requests

Ensure zero active user streams are dropped during worker failover.

Binary check

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

Dimension 2

Recovery Duration MS

Time in milliseconds required to migrate KV cache state to backup worker. • target: 380 • range: 0-500

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

  • Build a multi-agent resilience simulation using AutoGen framework.

  • Simulate node crash failures, network partition events, and GPU memory ECC faults.

  • Implement distributed state migration for continuous LLM generation streams.

  • Verify zero-data-loss failover boundaries during peak request throughput.

Resources and assets

Reference links and supporting material

Dataset notes

Fault injection event logs and stream state snapshots across distributed worker nodes.

Challenge setup

Evaluates dropped request rate and state recovery duration during hardware fault injection.

Challenge input

JSON scenario configuration specifying failure type and active stream count

AutoGen

Microsoft framework for orchestrating conversing agents in system simulations

Evaluated output

JSON result containing dropped_requests, recovery_duration_ms, and state_consistency_score

Evaluation checks
  • Ensure zero active user streams are dropped during worker failover.
  • Time in milliseconds required to migrate KV cache state to backup worker. • target: 380 • range: 0-500
Configured evaluation evidence
  • Benchmark: Distributed System Fault Tolerance Suite
  • Recovery Duration MS target: 380
  • 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.

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Configured tools

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

Evaluation contract

  • Zero Dropped Requests · Weight 1
  • Recovery Duration MS · Weight 1

Metrics

  • Recovery Duration MS · Target 380

Benchmarks

  • Distributed System Fault Tolerance Suite
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON scenario configuration specifying failure type and active stream count

    Output: JSON result containing dropped_requests, recovery_duration_ms, and state_consistency_score

    Execution harness: Python

Evaluation tools

  • AutoGen · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Distributed Inference Fault Injection & Recovery Simulator with AutoGen