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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

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 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.

How this agent runs

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

Checks for
  • 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
Proof of success
  • Benchmark: Distributed System Fault Tolerance Suite
  • Recovery Duration MS target: 380
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox (unavailable on Versalist)
View technical recipe

Configured tools

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

Evaluation contract

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

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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