The emergence of agentic artificial intelligence (AI) in 2025 marks a paradigm shift in software development. Agentic AI stands for proactive, intelligent systems that are designed to pursue complex tasks, make informed decisions, and act autonomously. It uses sophisticated and iterative reasoning to work through steps with limited supervision, adapting in real time to achieve organizational goals.

However, agentic AI applications bring new challenges owing to their autonomy, learning capabilities, and adaptability. Traditional quality engineering (QE) methodologies designed to verify specific functionalities are inadequate for ensuring the reliability and safety of such intelligent agents.

This blog explores key considerations for testing agentic AI applications. It emphasizes the shift from functional verification to goal achievement, adaptability assessment, bias mitigation, and long-term behavior analysis.

QE Approach for Agentic AI

Testing agentic AI calls for a combination of traditional testing principles with advanced AI-specific techniques. The following core strategies outline how QE must evolve to meet the challenges of testing agentic AI.

Shifting from functionality to goal achievement

• Beyond unit tests: While unit tests are helpful for component verification, the focus must shift to evaluating the agent’s effectiveness in achieving its intended goals.
• Scenario-based testing: Developing complex, real-world simulation scenarios is essential for determining the adaptability, learning effectiveness, and robustness of an agent. For example, an autonomous vehicle can be tested for scenarios involving heavy traffic, sudden road closures, or adverse weather conditions.

Assessing adaptability and learning

• Continuous evaluation: Monitoring the agent’s performance over time is essential to assess its learning progress, decision-making, improvement potential, and adaptability.
• Reinforcement learning tests: Reinforcement learning helps evaluate the ability of the agent to learn and adapt to dynamic environments. The agent learns through trial and error, receiving rewards for success and penalties for failures.

Mitigating bias and ensuring fairness

• Data diversity: Ensuring that training data is diverse and represents real-world scenarios is crucial.
• Bias detection tools: Mitigating bias is critical. AI systems can inherit and amplify societal biases as they are trained on extensive datasets. Tools like AI Fairness 360, Fairlearn, FAT Forensics, and Themis-ml can detect, quantify, and mitigate potential biases in the agent’s decision-making. They address data imbalances and discriminatory patterns, ensuring fair representation across diverse groups.
• Human-in-the-loop testing: Involving human testers in the evaluation process is essential for identifying and addressing biases and ethical concerns.

Evaluating long-term behavior

• Extended testing and monitoring: Long-term testing, auditing, and monitoring of the agent over time helps detect unintended consequences, unexpected decisions, and emergent behaviors.

Leveraging specialized tools and techniques

• AI QE frameworks: Specialized AI testing frameworks enable complex simulations, test case generation, and agent behavior analysis.
• Explainability techniques: Using techniques that analyze the internal workings of the agent helps identify potential issues in its decision-making.

Integrating human oversight and collaboration

• Human-in-the-loop systems: Integrating human oversight mechanisms enables timely intervention, provides guidance, and ensures safety.
• Cross-disciplinary collaboration: Collaboration among AI developers, testers, ethicists, and domain experts is essential for comprehensive and responsible testing.

Conclusion

QE in agentic AI applications requires a multifaceted approach beyond traditional software testing. It calls for a combination of established testing techniques and specialized AI testing tools. Prioritizing goal achievement, adaptability, bias mitigation, and long-term behavior ensures the development of safe and reliable AI systems. Continuous learning, innovation, and a strong emphasis on human oversight are key to addressing testing challenges in this rapidly evolving field. As agentic AI technology becomes more complex and autonomous, testing strategies must also keep pace.