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.

The Challenge of Evaluating AI Agents

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.

Traditional software testing focuses on deterministic outcomes and clear pass/fail criteria. AI agents, however, operate in probabilistic environments where the same input might produce different outputs, and success often depends on nuanced factors like context understanding and appropriate tool selection. This fundamental difference requires entirely new evaluation methodologies.

What's in the Starter Kit

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.

Getting Started with the Evaluation Framework

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. This containerized approach ensures consistent evaluation environments across different teams and organizations.

Why This Matters for Enterprise AI

As AI agents become more sophisticated and autonomous, the need for robust evaluation frameworks becomes critical. Organizations cannot simply deploy agents based on benchmark scores from isolated tests; they need to understand how agents will perform in their specific operational contexts.

The Evals for Agent Interop starter kit addresses this need by providing a framework that evaluates agents in scenarios that mirror real-world usage. By testing agents across multiple surfaces and measuring both technical correctness and qualitative factors, organizations can make more informed decisions about which agents to deploy and how to improve them.

This open-source approach also enables the broader AI community to contribute to and benefit from shared evaluation methodologies, accelerating the development of more capable and reliable AI agents across the industry.

Technical Architecture

The evaluation harness uses a modular architecture that allows for extensibility. The core components include:

• Scenario Runner: Executes predefined workflows that agents must complete
• Metrics Collector: Gathers quantitative data on agent performance
• AI Judge: Provides qualitative assessments using language models
• Leaderboard System: Aggregates results for comparative analysis

This architecture enables teams to add new evaluation scenarios, metrics, and judging criteria without modifying the core framework.

Industry Context

The release of Evals for Agent Interop comes amid growing interest in AI agent evaluation frameworks. Other major tech companies and research institutions are developing similar tools, reflecting the industry's recognition that agent evaluation is a critical bottleneck in AI deployment.

Microsoft's approach emphasizes practical, enterprise-focused evaluation scenarios rather than abstract benchmarks. This aligns with the needs of organizations that want to deploy AI agents in production environments where reliability and predictability are paramount.

Future Directions

Microsoft has indicated plans to expand the starter kit with additional scenarios, more sophisticated evaluation metrics, and support for a wider range of agent architectures. The open-source nature of the project means that community contributions could significantly accelerate this evolution.

Potential future enhancements might include:

• Support for voice and video-based agent interactions
• Integration with enterprise identity and security systems
• Automated regression testing capabilities
• Performance benchmarking under different load conditions

The Evals for Agent Interop starter kit represents a significant step toward making AI agent evaluation more systematic and accessible. As organizations continue to adopt AI agents for critical business functions, tools like this will become increasingly important for ensuring reliability, safety, and effectiveness.