AI Agent Testing Challenges, Best Practices by Agentforce

Key Takeaways

AI agent testing must evaluate intent, subagent selection, actions, and response quality.
A structured AI agent testing framework helps test diverse scenarios and identify regressions.
Agentforce Testing Center enables scalable testing with automated test generation and quality evaluation.
Combining deterministic checks with AI agent evaluation provides a more complete view of agent performance.
Regular testing helps enterprises build reliable, compliant, and production-ready AI agents.

AI agents are moving from experimental assistants to systems that answer customer questions, retrieve knowledge, make decisions, and execute business actions. That shift creates a new quality challenge: an agent can produce a convincing response while still selecting the wrong subagent, using the wrong information, or failing to complete the intended task.

Traditional software testing remains important, but AI agent testing requires additional evaluation. Teams must assess not only whether a workflow works, but also whether the agent understands the request, chooses the right path, follows instructions, and produces a reliable outcome.

The scale of agentic work makes this increasingly important. Salesforce ^[1] reported that Agentforce customers consumed more than 20 trillion tokens in Q4 2025, representing a 400% year-over-year increase, while organizations recorded 1.79 billion agentic work units during the quarter. These figures illustrate the growing volume of AI-driven work that organizations need to evaluate and manage.

This is where Agentforce Testing Center helps. Salesforce’s testing capabilities allow teams to generate test scenarios, evaluate agent behavior at scale, and identify issues before deployment. The newer Agentforce Studio workflow also supports conversation-level testing, custom evaluations, and inline editing.

In this guide, we explain what AI agent testing is, why it is challenging, how to build an effective testing strategy, and how Agentforce Testing Center helps teams evaluate AI agents more systematically.

Table of Content

What Is AI Agent Testing?
Why Is AI Agent Testing Challenging?
Building an AI Agent Testing Framework
AI Agent Test Cases: Examples and What to Include
What Is the AI Agent Testing Best Practices?
What Is LLM-as-a-Judge in AI Agent Testing?
Agentforce Testing Center: What’s New in 2026?
A Smart Test Strategy for Agentforce
Agentforce Testing Center vs. Traditional Software Testing
Why AI Agent Testing Matters for Enterprise and Regulated Industries
Final Thoughts

What Is AI Agent Testing?

AI agent testing is the process of evaluating whether an AI agent understands user requests, selects the appropriate subagent, executes the expected actions, retrieves accurate information, and produces responses that meet defined quality standards.

“Agentforce is helping businesses create a limitless workforce. To deliver this value fast, CIOs need new tools for testing and monitoring agentic systems”

– Adam Evans, Former EVP and GM for Salesforce AI

Unlike a simple chatbot that only generates text, an AI agent may:

Interpret a user’s intent.
Select a subagent.
Retrieve information from connected sources.
Call tools or business actions.
Maintain context across multiple turns.
Produce a response that must be accurate, complete, and relevant.

A practical AI agent for evaluation therefore needs to measure both what the agent does and what the agent says.

Why Is AI Agent Testing Challenging?

Traditional testing approaches rely heavily on predictability. It includes known inputs, expected outputs, and consistent system behavior almost every time. Examples of traditional testing methods include unit testing, integration testing, regression testing, and behavioral testing. Here, the paths to testing are finite, and expected behaviors are also well known.

However, AI agents don’t work this way. AI agents introduce several testing challenges that traditional software testing alone does not fully address. The 2025 AI Agent Index ^[2] found that only 4 of 13 AI agents operating at frontier levels of autonomy disclosed any agent-specific safety evaluations, while 25 of the 30 agents studied disclosed no internal safety results.

1. Non-Deterministic Responses

Challenge: The same user request may produce different responses depending on context, model behavior, and available information.

What to test: Whether the response remains accurate, relevant, and aligned with the agent’s instructions.

Recommended approach: Use a combination of deterministic assertions and quality evaluations. Test multiple variations of the same intent, including ambiguous wording, incomplete requests, and different user contexts.

2. Dynamic Subagent and Action Selection

Challenge: An agent may select the wrong subagent or execute an incorrect action even when its response sounds reasonable.

What to test: Whether the expected subagent is selected and whether the expected actions are executed in the correct sequence.

Recommended approach: Define expected subagent and action outcomes for each test case. Validate the agent’s actual behavior against those expectations.

3. Knowledge Accuracy and Retrieval

Challenge: An agent may retrieve irrelevant information, miss an important source, or provide an answer that is not supported by the available knowledge.

What to test: Whether the agent retrieves the right information and communicates it accurately.

Recommended approach: Create knowledge-based test cases from the agent’s connected data sources. Evaluate response accuracy and, where applicable, whether the response includes the expected citations.

4. Multi-Turn Conversations and Context

Challenge: An agent may answer the first question correctly but lose context when the user asks a follow-up.

What to test: Whether the agent maintains the conversation context, uses earlier information correctly, and completes the intended task across multiple turns.

Recommended approach: Test complete conversations rather than only isolated utterances. Include follow-up questions, clarifications, and changes in user intent.

5. Safety, Compliance, and Instruction Adherence

Challenge: An agent may respond incorrectly to sensitive requests, fail to follow instructions, or take action that should require additional verification.

What to test: Whether the agent follows configured instructions and handles sensitive or ambiguous requests appropriately.

Recommended approach: Include edge cases, negative scenarios, and business-specific compliance checks in the test suite.

Discover How Agentforce Accelerates Safe AI Agent Deployment

Building an AI Agent Testing Framework

To launch an agent that’s trustworthy and reliable, you want to thoroughly test it. The classic layered testing pyramid is still the way to go. But with a twist. Here’s how to get started:

Testing layer	What it validates	Example
Unit testing	Individual components and basic behavior	Does the agent correctly identify a leave request?
Integration testing	Connections between the agent and business systems	Does the agent trigger the correct workflow or API?
Behavioral testing	Whether the agent achieves the intended goal	Does the agent complete the requested task?
Subagent testing	Correct routing and intent recognition	Does the request reach the intended subagent?
Action testing	Correct action selection and execution	Does the agent call the expected action?
Knowledge testing	Retrieval and response accuracy	Does the agent provide the correct policy information?
Conversation testing	Context retention and multi-turn behavior	Does the agent handle a follow-up request correctly?
Quality evaluation	Completeness, coherence, conciseness, and instruction adherence	Is the response accurate and easy to understand?

The objective is not to replace traditional testing. It is to extend it so that teams can evaluate the full agent lifecycle, from individual behavior to end-to-end task completion.

AI Agent Test Cases: Examples and What to Include

AI agent test cases are structured scenarios used to evaluate whether an agent understands a request, selects the right subagent, executes the expected actions, and produces an accurate response.

A good test case should include:

Input / utterance
Expected subagent
Expected action
Expected response or outcome
Evaluation criteria
Context or conversation history, when relevant

Salesforce’s newer testing workflow supports test cases based on subagents and actions, knowledge sources, and custom scenarios uploaded through a CSV template.

What Is the AI Agent Testing Best Practices?

Follow a structured AI agent testing framework: test intent recognition, subagent selection, action execution, response quality, and multi-turn behavior across realistic scenarios. Use repeatable test cases, clear evaluation criteria, and continuous monitoring to improve accuracy, reliability, and compliance.

1. Combine Deterministic Checks with Quality Evaluation

Deterministic checks are useful for validating exact outcomes, such as subagent selection and action execution. Quality evaluation can assess whether the response is accurate, complete, coherent, and concise.

The strongest approach is to use both. LLM-based evaluation should complement deterministic checks, not replace them. Salesforce’s testing capabilities support both assertion-based evaluation and LLM-based response quality scoring.

2. Test Diverse Scenarios

Do not test only the ideal user journey. Include:

Different ways of asking the same question
Ambiguous or incomplete requests
Edge cases
Negative scenarios
Multi-turn conversations
Knowledge-based questions
Requests that should trigger a different subagent

3. Test in a Sandbox Environment

AI agents can interact with CRM data and execute business actions. Testing in a sandbox helps reduce the risk of unintended changes to production data.

Salesforce recommends using Testing Center in a sandbox and notes that tests can consume requests and credits.

4. Retest After Changes

Agent instructions, knowledge sources, actions, and model behavior can change over time. A test suite should be rerun after significant updates to identify regressions and new failure modes.

5. Measure More Than Pass/Fail

A useful AI agent evaluation should consider:

Response accuracy
Subagent recognition
Action execution
Knowledge retrieval
Completeness
Coherence
Conciseness
Latency
Instruction adherence
Task resolution

These metrics help teams understand why an agent failed, not just whether it failed.

What Is LLM-as-a-Judge in AI Agent Testing?

LLM-as-a-Judge is an evaluation approach in which one language model assesses the output of another against defined criteria.

In AI agent testing, it can help evaluate:

Response accuracy
Relevance
Completeness
Coherence
Instruction adherence
Brand voice or tone
Task resolution

For example, a custom scorer could evaluate whether a customer-service response is polite, accurate, and complete.

However, LLM-based evaluation should complement deterministic checks rather than replace them. Deterministic checks remain important for validating exact subagent selection, action execution, required fields, and other measurable outcomes.

Salesforce’s custom scorer workflow allows teams to define evaluation criteria, scoring thresholds, and business-specific quality measures.

Agentforce Testing Center: What’s New in 2026?

The newer Agentforce Studio testing workflow expands the capabilities of Agentforce Testing Center beyond basic batch testing.

“To help engender trust in AI, every business investing in an agent strategy will need to think through how they pressure test an autonomous AI agent that can reason, retrieve data, and use tools. Salesforce is instilling confidence by using AI to test hundreds of variations of an agent’s interaction in parallel. With these enhancements and support for Agentforce and Data Cloud in familiar testing environments, this Testing Center will help Salesforce customers manage the unique needs of testing AI agents.”

– Keith Kirkpatrick, VP and Research Director, The Futurum Group.

1. AI-Generated Test Cases

Testing Center can generate test cases from an agent’s subagents and actions. This helps teams create scenarios that reflect the agent’s configured behavior.

2. Knowledge-Based Test Generation

Teams can generate Q&A-style test cases from content connected through the Agentforce Data Library. This is useful for evaluating whether the agent retrieves and communicates information accurately.

3. Response Quality Evaluation

Testing Center supports evaluation of response accuracy and quality metrics such as completeness, coherence, conciseness, latency, and instruction adherence.

4. Subagent and Action Validation

The testing workflow can evaluate whether the agent selects the expected subagent and executes the expected actions.

5. Conversation-Level and Multi-Turn Testing

The newer workflow supports testing complete conversations rather than only isolated responses. This helps teams evaluate whether the agent maintains context and achieves the intended goal across multiple turns.

6. Custom Scorers and Evaluations

Teams can define custom evaluation criteria for business-specific requirements, such as brand voice, structured outputs, or other quality measures.

7. Agentforce Studio Integration and Inline Editing

The newer workflow integrates testing with Agentforce Studio and supports inline editing of test cases. This makes it easier to update expected values, refine scenarios, and rerun tests after changes.

Salesforce distinguishes the newer Agentforce Studio testing workflow from the legacy Testing Center in Setup. The article should not imply that every capability is available in the same interface.

A Smart Test Strategy for Agentforce

But mastering this framework can take a while. Instead, you can leverage a ready-made solution. And this is what Agentforce aims to solve. You can test a large number of scenarios using Agentforce Testing Center. It’s a tool for testing AI agents built on Salesforce. It supports batch testing, which lets you quickly evaluate multiple requests in a single run. For example, check intent recognition: Test 50-60 different ways users might ask to reset a password using the insurance agent. Assess if the agent handles them correctly.

To make the process even quicker, you can use Gen AI to create test cases for you. This can help you optimize testing time and quickly launch agents.

Here’s how to test an AI agent using the Agentforce Testing Center:

Step 1

Make sure that Agentforce is activated in the Sandbox. So, you’re testing the agent in a safe environment to avoid impacting production data.

Step 2

Create a new test. Test multiple topics and actions at the same time in your sandbox. First, grab a testing template (CSV file) designed for batch testing. It would include parameters like utterance, expected topic, expected actions, and more. Leverage Gen AI to generate different test scenarios for you.

Step 3

Use the test results. The test results indicate which cases were successful and which ones were unsuccessful. Subsequently, you can identify what caused the failure, make specific tweaks in the utterances, and rerun the test.

To learn more, watch the complete tutorial here.

Ready to Test Your First AI Agent? Start with Agentforce Testing Center

Agentforce Testing Center vs. Traditional Software Testing

Traditional testing is still essential. The difference is that AI agents require additional evaluation methods to account for non-deterministic responses and dynamic behavior.

Traditional Software Testing	Agentforce Testing Center
Known inputs and expected outputs	Variable inputs and context-dependent responses
Fixed workflows	Dynamic subagent and action selection
Primarily deterministic assertions	Deterministic checks plus quality evaluation
Unit, integration, and regression testing	Agent behavior, action execution, knowledge retrieval, and conversation testing
Test cases are often manually authored	Test cases can also be AI-generated
Results focus on pass/fail	Results can include quality scores and evaluation metrics

Why AI Agent Testing Matters for Enterprise and Regulated Industries

For enterprise and regulated industries, AI agent testing is not just a quality exercise. It is part of managing operational risk.

Organizations should evaluate whether agents:

Retrieve information from approved knowledge sources.
Follow configured instructions.
Execute only the intended actions.
Handle sensitive requests appropriately.
Maintain context across conversations.
Avoid unintended changes to production data.
Meet business-specific quality and compliance requirements.

Testing in a sandbox is especially important when agents interact with CRM data or other business systems. Salesforce recommends sandbox testing to avoid issues with production data.

Final Thoughts

The era of AI agents is upon us. And an AI agent is never “done”. Language changes. User behavior evolves. Operational priorities shift. So, while integrating this feedback is critical, it can be time-consuming using traditional testing strategies. But with the Agentforce Testing Center, you can test and deploy AI agents confidently. You no longer need to make tough decisions between speed and safety.

With Agentforce, you can do both while catering to today’s rising user expectations. It’s not about testing harder. But about testing smarter. Alternatively, you can also skip the complexities of DIY AI with our range of Agentforce services.

References:

AI Agent Testing: Challenges, Best Practices, and How Agentforce Testing Center Solves Them