3 Essential Testing Steps That Can Make or Break Your AI Agents

Picture this: Your AI agent is helping a customer with a routine question over chat — suddenly, the conversation is derailed and your agent is sharing sensitive data and providing irrelevant information. Your customer is left confused, frustrated, and worried about how your organization is using their data. 

This doesn’t need to happen, and it shouldn’t. 

In a world of AI hallucinations and IT breaches, it’s no surprise that 64% of consumers believe that companies are being reckless with their data. To make sure that your AI agents meet only the most robust quality and security requirements, a scalable, unified testing strategy is a must to deliver total confidence before you hit “deploy”.

Why AI agent testing matters

With great innovation, comes great responsibility. AI agents operate a little differently compared to traditional applications, and because of this, they present new challenges to look out for in deployment.

Testing matters for a few reasons:

• AI agents are non-deterministic. If you ask them the same question twice, you may get a variation of your answer. There is also the possibility of AI hallucinations, which can be mitigated with things like using a trusted, unified platform, writing more specific prompts, and of course, testing. 
• Security vulnerabilities may emerge. Insufficient testing leaves your organization vulnerable to security issues like prompt injection. Malicious users can exploit conversational gaps to manipulate the agent into revealing sensitive company data, proprietary business logic, or internal system details.
• Brand policies should be followed. The agent may produce responses that are not aligned with your brand’s voice, ethical guidelines, or even local regulations. This can range from offensive language to providing illegal or dangerous advice.

How to test thoroughly

Verifying that your agents are ready to meet your customers is more than just a formality. It requires these key strategic elements to ensure you’re deploying only the most secure, high-performing agents.

Step 1: Choose your sandbox

A key reason for AI agent testing is to proactively protect from security vulnerabilities. Protecting your data is not just a checkbox at the end of the development process, or even at just the testing stage. By taking on a “shift left security” strategy, or integrating security as early as possible in the development process, you can confidently deploy trusted agents. And for most teams, that starts at environment setup and test data preparation, all of which happens in your sandbox. 

Not all sandbox environments are the same though. Salesforce offers a tiered approach to Sandboxes because at different stages of the agent and application lifecycle management (ALM) process, different needs must be met. For data-aware validation, for example, where you’re testing an agent’s logic against realistic data samples to verify compliance, governance, and initial accuracy — a Partial Copy Sandbox is sufficient. To validate full-stack performance reliability, however, a Full Copy Sandbox is the only suitable environment. This is because it mirrors your entire production org, including all data and meta-data, ensuring test results accurately reflect real-world performance. Even if you already own one Full Copy Sandbox, you may need a separate, dedicated environment to isolate the specific high-volume throughput and performance required for Agentforce testing without introducing risk to other vital testing processes.

Step 2: Ensure seamless, scalable deployment

To understand how your agent will behave in the real world, you’ll need to perform two types of tests. These tests ensure both the quality of your agent’s response and the stability of your underlying system under load. 

The first, is Utilization Testing, also known as the “quality check” because it focuses on accuracy and correctness. This type of testing asks questions like, “Is this agent working how I want it to?” and “Is the agent providing high-performing and consistent responses?” Using agent testing specific tools, like Agentforce Testing Center, allows you to batch test agents, compare test runs, and track performance using key metrics to manage their non-deterministic behavior.

Performance Testing, or scale testing, is used to evaluate speed and quality. “Can my agent handle high volume situations?” to ensure that your agent will maintain peak performance, even during high traffic moments. This vital process is supported by Scale Test, which allows you to enhance your sandbox to a production-scale configuration, and Scale Center, which provides the necessary performance and scalability insights for diagnosis and optimization.

These tests should be performed in an environment secured by data masking and seeded with realistic data to ensure both accuracy and confidentiality.

Step 3: Continue to improve

With the non-deterministic nature of AI, AI correctness can be subjective. Over time, your agent’s performance may change due to a number of factors — new data collected, model updates, new user behavior, and more. In order to maintain user trust and consistent, quality experiences, regular maintenance is a must, even after deployment. 

Agentforce Command Center is a tool to help you supervise your AI agents. Its deep observability captures live health monitoring, consumption tracking, and adoption analytics. Paired with Agentforce Testing Center, which lets you track performance and enables iterative refinement of agent prompts and configurations based on test results, you can help Agentforce meet accuracy and compliance requirements. 

Test today, deploy confidently today 

Building and deploying a powerful, robust Agentforce is all part of becoming an Agentic Enterprise, but the stakes — security, compliance, and customer trust — are high. Don’t let the promise of AI be undermined by uncertainty. A strong AI agent testing strategy isn’t just a best practice, it’s a non-negotiable investment.