I'm about to begin a testing role on a team that builds AI and Power Platform solutions, including Copilot Studio agents. My background is in software engineering, so I'm comfortable with programming, databases, software development, Python, and Java, but I'm relatively new to professional software testing.
The team currently relies mostly on manual testing while it develops a testing strategy and learns the capabilities and limitations of its AI tools. Longer term, they plan to introduce automated testing, so I'd like to understand both effective manual testing and how these processes can eventually be automated—not just learn to click through predefined test cases.
I have about two weeks before I start. What concepts, skills, tools, or resources should I prioritize to become as prepared as possible for testing AI agents and Power Platform solutions?
2 Answers
The biggest adjustment with AI testing is dealing with nondeterministic behavior. A response can pass one time and fail the next, so a single successful run doesn’t prove very much. Think in terms of repeatable test sets, expected behavior ranges, and measurable criteria such as relevance, factual accuracy, clarity, safety, consistency, refusal behavior, and correct handling of unknown questions.
For the two weeks you have, focus on core testing skills first: writing clear test cases, exploratory testing, boundary and negative testing, requirements traceability, defect reporting, and risk-based prioritization. Then apply those techniques to AI-specific cases such as ambiguous prompts, prompt injection, conflicting instructions, sensitive data, unsupported requests, long conversations, context loss, and unexpected user phrasing.
Look at how AI evaluations and benchmarks measure quality. You don’t need to become an ML researcher, especially if the team is building agents through a low-code platform, but understanding the ideas behind benchmarks will help you design better tests.
Instead of trying to prove that an agent can never behave incorrectly, define categories of behavior and collect enough results to identify patterns and acceptable error rates. Build a small evaluation dataset with normal requests, edge cases, unsafe requests, ambiguous wording, and questions where the correct response is to admit uncertainty. Record the prompt, expected outcome, actual response, pass or fail decision, and any severity or risk rating.
Also learn the platform’s agent configuration, topics, connectors, permissions, knowledge sources, and fallback behavior. That will help you distinguish a problem with the agent’s instructions from a data, integration, authentication, or platform issue. Basic API testing and scripting in Python will also prepare you for automating repeated checks later.
Since the team uses Copilot Studio and currently tests manually, I was wondering whether traditional ML testing would still be relevant. It sounds like the evaluation principles are useful even when the agent itself is assembled through a low-code interface.
That makes sense. I’ve been looking at response evaluation, but I wasn’t sure how to build the broader testing foundation around it.