Agentforce Testing: Ensuring Quality & Security of LLM-Powered Apps

Agentforce, Salesforce AI’s newest offering, recently announced by Salesforce, represents a bold and revolutionary approach to leveraging AI for Salesforce customers. At its core, it comprises a suite of AI-powered agents designed to automate tasks and augment employee capabilities across various business functions such as sales, service, marketing, and commerce. These agents operate autonomously, analyzing data, making decisions, and taking action without constant human intervention. This leading LLM application is made possible through the integration of Salesforce’s Data Cloud, the Atlas Reasoning Engine, and various automation tools.

However, the disruptive nature of this solution introduces new challenges concerning quality standards. Traditional applications, whether code-based or no-code, operate deterministically, allowing predictable outcomes that follow specific rules. Salesforce Testing methods for these systems have been built around that predictability. In contrast, LLM applications like Agentforce rely heavily on LLMs (Large Language Models), which introduce complexity because they are non-deterministic and depend on dynamic data.

Key Risks in Transitioning from Development to Production

Moving from a controlled development environment to live production with Salesforce AI agents presents several risks:

  • Unpredictable Real-World Data: While agents are trained on existing data, in production they encounter new, diverse, and often unpredictable data. This can lead to unexpected outputs or behaviors if the agent isn’t prepared to handle such variability.
  • Integration Complexity: Agentforce agents don’t work in isolation. They must integrate seamlessly with Salesforce systems like Data Cloud, Flow, and external applications. Ensuring this integration holds up under real-world loads is crucial.
  • Consistent Brand Representation: As these agents interact with customers, they represent the brand. Any inconsistency in tone, style, or accuracy can harm the brand’s reputation. The challenge increases as the agents become more autonomous.
  • Ethical Implications and Bias: AI agents may reflect biases present in the data they were trained on. If not identified and mitigated, these biases could cause significant issues once the agents are in production.

The Importance of Pre-Release and Continuous Testing

Rigorous testing is critical, both before and after an agent’s release, to address these challenges.

How to Validate the Quality of LLM Solutions and Agents

Testing LLM solutions like Agentforce requires a departure from traditional software testing. Since LLMs generate diverse, context-sensitive responses, the goal is not to find exact matches but to evaluate the quality, relevance, and appropriateness of outputs.

Rather than looking for precise responses, testing must focus on establishing guardrails—standards that responses must meet, such as factual accuracy, coherence, relevance, and ethical compliance. These guardrails ensure responses align with expectations without compromising on the flexibility LLMs offer.

One innovative approach is leveraging LLMs themselves to evaluate other LLM outputs. This allows a nuanced assessment of factors like semantic similarity, logical consistency, and adherence to criteria that rule-based systems struggle to capture.

Example Guardrails for Testing LLM applications:

  1. Responses must include relevant data.
  2. Responses should not contain personal information.
  3. Replies should be professional and provide a solution to the problem at hand.

Panaya Test Automation: A Solution for Testing Agentforce and LLM Applications

Panaya Test Automation has emerged as a game-changer for testing LLM-based solutions like Agentforce. Its internal LLM engine, seamlessly integrated into the test automation process, allows for both functional behavior and response quality testing—offering a comprehensive testing solution for AI systems.

Best Practices for Testing LLMs and Agentforce with Panaya Test Automation

  1. Semantic Quality Assertions: Ensure that AI responses align with pre-established quality standards for relevance and appropriateness.
  2. Repeatable Execution: Run tests repeatedly to collect metrics over time, helping identify patterns and improvements in AI performance.
  3. Diverse Input Testing: Test the AI across a wide range of inputs and personas to ensure it can handle different scenarios and user types.
  4. Functional Behavior Verification: Verify that the AI is performing intended actions correctly, alongside assessing response quality.
  5. Integration Testing: Ensure smooth interactions between the AI and other systems or data sources.
  6. Ongoing Monitoring: Continuously validate LLM applications even after deployment, as their performance may change based on evolving data or context.
  7. Bias and Ethical Testing: Implement tests designed to identify biases or ethical concerns in AI outputs.
  8. Security and Data Privacy Checks: Ensure AI adheres to data protection regulations and security protocols.
  9. User Experience Testing: Assess responses from a user perspective, ensuring clarity, helpfulness, and consistency with the brand’s voice.

Panaya Test Automation excels in implementing these practices, combining LLM-based quality assessments with traditional functional testing. This comprehensive approach helps ensure the reliability and effectiveness of AI solutions like Agentforce in real-world applications.

By following these best practices and leveraging Panaya Test Automation, organizations can confidently deploy and maintain high-quality AI solutions, mitigating risks and optimizing performance in production environments.

Start changing with confidence

Book a demo
Skip to content