Microsoft AutoGen
For Microsoft AutoGenAI Platform

Autogen Multi Agent Teams

AutoGen · Microsoft AutoGen

Multi-agent Framework — Microsoft AutoGen

Evaluates Microsoft AutoGen's Multi-agent Teams (RoundRobin / Selector / Swarm / MagenticOne) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Multi-agent Framework eval coverage.

About Microsoft AutoGen

Microsoft is a global technology company and a leading cloud and AI provider. Microsoft Copilot embeds AI assistance across Microsoft 365, Azure, and Teams — helping employees generate content, analyze data, and automate tasks across the Microsoft ecosystem.

Employees

~221,000

Industry

Enterprise Software & Cloud

Headquarters

Redmond, WA

Sample tests· showing 3 of 9

#InputExpected behaviorCheck
01

RoundRobinGroupChat with participants=[planner, coder, reviewer]. Operator expects planner→coder→reviewer→planner alternation but observes planner speaking twice in a row when coder emits an empty response.

RoundRobinGroupChat advances strictly through participants in declared order — empty agent responses still count as that agent's turn. If a 'speak again' is needed, either re-prompt within the same turn or switch to SelectorGroupChat with allow_repeated_speaker. Do not rely on side effects to skip …

Pass / FailAi Platformmedium
02

SelectorGroupChat with selector_prompt that references participant names but each participant's description= is left blank. Selector LLM picks the same agent every turn.

Selector picks the next speaker using participant.description — populate each agent's description with a one-sentence capability statement (e.g. 'researcher: web search and fact extraction'). Without descriptions the selector has nothing to disambiguate on and degenerates to a single speaker.

Pass / FailAi Platformhigh
03

SelectorGroupChat with allow_repeated_speaker=False. Selector LLM picks the same agent that just spoke.

With allow_repeated_speaker=False, AutoGen re-asks the selector (or falls back to a different participant) so the same speaker cannot speak twice in a row. Verify the actual transcript respects this by inspecting the message source field. To permit the same agent to refine its own answer, set allow…

Pass / FailAi Platformmedium

Unlock full benchmark

6 more test cases

Use this benchmark

How this eval is graded

Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

Rubric criteria

  • Autogen
  • Ai Platform
  • Multi Agent Teams

Recommended for

AutoGenMicrosoft AutoGen customers

Works with

Related evals

Frequently asked questions

What does the Autogen Multi Agent Teams eval for Microsoft AutoGen AutoGen test?+

Evaluates Microsoft AutoGen's Multi-agent Teams (RoundRobin / Selector / Swarm / MagenticOne) across 9 scenario-based test cases, each graded against an expected-behavior rubric by an LLM judge, from Corsac's Multi-agent Framework eval coverage.

How is the Autogen Multi Agent Teams eval scored?+

The judge rubric: Grade against expected.ideal_behavior and expected.rubric. Per-criterion pass requires mean >= 4.0 and no criterion below 3.

How many test cases does this eval pack include?+

The Autogen Multi Agent Teams pack for Microsoft AutoGen AutoGen contains 9 test cases. 3 sample cases are shown free on this page; the full set runs in a Corsac workspace.

How do I run this eval?+

Sign up for Corsac, connect your model or agent endpoint, and run the Autogen Multi Agent Teams pack as-is or after customizing thresholds. Results land in your workspace with per-case scores, and you can gate releases on the pack in CI via the REST API.

Run this eval in your workspace

Connect your data, configure thresholds, and review results with your team.