Microsoft Open Sources Evals for Agent Interop Starter Kit to Benchmark Enterprise AI Agents

Microsoft has introduced Evals for Agent Interop, an open-source starter kit designed to help developers and organizations evaluate how well AI agents interoperate across realistic digital work scenarios. The kit provides curated scenarios, representative datasets, and an evaluation harness that teams can run against agents across surfaces like email, calendar, documents, and collaboration tools. This effort reflects an industry shift toward systematic, reproducible evaluation of agentic AI systems as they move into enterprise workflows.

Enterprises building autonomous agents powered by large language models face new challenges that traditional test approaches were not designed to address. Agents behave probabilistically, integrate deeply with applications, and coordinate across tools, making isolated accuracy metrics insufficient for understanding real-world performance. Agent evaluation has emerged as a critical discipline in AI development, particularly in enterprise settings where agents can affect business processes, compliance, and safety. Modern evaluation frameworks strive to measure not just end results but behavioral patterns, context awareness, and multi-step task resilience.

The Evals for Agent Interop starter kit aims to give teams a repeatable, transparent evaluation baseline. It ships with templated, declarative evaluation specs (in form of JSON files) and a harness that measures signals such as schema adherence and tool call correctness alongside calibrated AI judge assessments for qualities like coherence and helpfulness. Initially focused on scenarios involving email and calendar interactions, the kit is intended to be expanded with richer scoring capabilities, additional judge options, and support for broader agent workflows.

Microsoft also includes a leaderboard concept in the starter kit to provide comparative insights across “strawman” agents built using different stacks and model variants. This helps organizations visualize relative performance, identify failure modes early, and make more informed decisions about candidate agents before broad rollout.

The GitHub repository hosts the starter code under an open-source license. It presents the evaluation artifacts and harness components needed to run tests and compare multiple agent candidates head-to-head. The project scaffolds a baseline evaluation suite, and developers can tailor rubrics to their specific domains, re-run tests, and observe how agent behavior shifts under different constraints.

To get started, developers can clone the Evals for Agent Interop repository, run the included evaluation scenarios to baseline their agents, and then customize rubrics and tests to reflect their workflows. The kit is deployed as a Docker compose set of three images, making it easy for developers to execute it locally.