AI Agents are transforming how organisations deliver services, automate tasks and support users. As they become more capable, ensuring they behave consistently and reliably is critical, especially if they are customer facing or embedded in business-critical workflows. 

Unlike traditional software, AI Agents are probabilistic which means the same input won’t always yield the same output. This flexibility is powerful but introduces risk. Inconsistent or incorrect behaviour erodes trust, slows adoption and undermines the value of your AI investment. 

As you build Agents with platforms like Microsoft Copilot Studio, this raises the question, how can you test something that doesn’t always behave the same way? 

Why traditional methods fall short  

Conventional tests assume a single “correct” output per input. AI Agents, however, can take different decision paths and still arrive at valid but non‑identical results. This makes rigid, rule-based testing less effective as it quickly becomes impossible to account for all possible results. Therefore, a new approach is needed, one that tests for meaning not just exact matches. 

Make sure your Agent works as you build 

Manual testing is valuable early on: it exposes obvious issues; lets you explore behaviour and speeds up iteration. But manual testing isn’t scalable. As you expand your Agent to include more topics, tools and workflows, manual testing every scenario becomes time consuming and error‑prone. Therefore, automated testing is essential and must account for the flexible nature of AI. 

Automated testing with Agent Evaluation 

Agent Evaluation enables structured, automated testing directly in Copilot Studio. You create test sets and run them in batches to see how your Agent performs, supporting faster development through rapid iteration. 

In our experience, for conversational Agents, the standout capability is semantic analysis: instead of checking for exact text matches, the tool assesses whether the Agent’s response semantically means the same thing as the expected result. In our experience, this is more reliable than keyword checks, it catches real issues like incomplete or incorrect answers without flagging legitimate variations. 

Today, Agent Evaluations are best for basic, single‑turn workflows. They don’t yet fully cover tool usage or multi‑step conversations, which makes validating complex behaviour harder. The feature is in preview and continues to roll out roadmap improvements aimed at addressing these limitations. 

Using Copilot Studio Kit for more complex Agents 

Despite the name, Copilot Studio Kit is separate from Copilot Studio and is developed by the Power CAT team. It offers deeper testing for complex, enterprise‑grade Agents: 

• Test how your Agent uses tools and knowledge sources  

• Run multi-turn conversations to simulate real user journeys  

• Validate that the Agent selects the right action or tools for a task  

• Analyse test results using data from Azure and Dataverse  

It requires additional setup and uses Copilot credits, but the time saved and confidence gained typically justify the cost. In particular, multi‑turn testing has helped us improve answer quality and consistency across longer conversations.  

Marra’s best practices for reliable Agents 

Our team has found that combining automated testing with human review of the results gives the best results. To ensure your Agents behave well and deliver value, we recommend:  

• Use manual testing early to explore behaviour  

• Transition to automated testing for simple single turn scenarios using Copilot Studio Evaluations  

• Use semantic analysis to check for meaning, not just wording  

• Setup in-depth testing inside of Copilot Studio Kit for full user journeys and all Agent functionality  

• Build test sets from real user transcripts to mimic actual usage  

• Run tests sets repeatedly to check durability of the Agent  

• Run all tests after each version of an Agent is developed to ensure functionality remains intact  

Making testing essential  

Testing is an essential part of the AI Agent development process, not a nice to have. It’s a strategic enabler that ensures consistent Agent behaviour to build trust with users. Robust testing reduces the risk of failed deployments and reputational damage, while introducing faster, repeatable tests shortens release cycles.  

Robust testing helps you stay ahead of change across many moving parts: bug fixes, new features, core model updates, system prompt adjustments, downstream automation changes and evolving knowledge sources.  In short, testing turns a promising AI idea into a dependable solution. 

If you want support with testing your AI Agents then get in touch. If you’d like to learn more about Agentic AI, visit our AI hub.  

Written by Kyle Anderson, Power Platform Developer