Comprehensive testing begins with clearly defining what success looks like for your
agent. Preparation also involves breaking down the agent into components, like subagents and
actions, and identifying the most relevant data for evaluation. After you have this information,
you can move on to designing effective structured test scenarios that reflect real-world
interactions.
Before testing begins, clearly define what constitutes success for your AI agent.
Set goals linked to business value: Establish specific, measurable, attainable, and
relevant goals for your agent's performance, directly linked to the business objectives
you identified for your agent.
Deconstruct the agent: Break down the agent into components, such as subagents and
actions. Testing these parts individually helps to pinpoint inefficiencies at their
source.
After you’ve outlined your objectives, the next step is to create a testing strategy. A
solid strategy includes:
Identifying test scenarios and setting evaluation parameters.
Running tests and validating the results.
Making evidence-based adjustments and repeating tests until your agent is working as
expected.
Test on a regular cadence to make sure that your agent is still performing the way you
intend.
Create Test Scenarios and Identify Data Requirements
A testing scenario evaluates how well an AI agent performs in a realistic situation. It
simulates a real-world interaction and helps you test whether the agent understands the user
input correctly, selects the right subagent, runs appropriate actions, and responds
accurately, completely, and in the right tone. A strong testing scenario includes:
Scenario description: A short narrative of the situation or context, such as a
customer who just received their device and is confused by an error code.
Test utterance: The actual phrase or question the user enters.
Expected subagent: The subagent that the agent is expected to select based on
either its configuration or deterministic routing transitions.
Expected action: The specific action that the agent is expected to take in
response, like “lookup information about products and policies” or “escalate the
conversation to a service rep.”
Expected response: A brand-appropriate reply based on validated information. The
agent’s wording can vary, but verify that the tone feels right and the response is
accurate.
Utterance variations: The different ways that customers are likely to phrase a
question or request. Include utterances that vary based on language differences and
different customer emotional states as well as utterances that change the conversation
topic.
Multi-turn: A conversation that extends beyond one question and response. It’s
important to test how the agent handles requests in a continued conversation.
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