How to automate the testing of AI agents

""Testing agentic AI is no longer QA, it is enterprise risk management, and leaders are building digital twins to stress test agents against messy realities: bad data, adversarial inputs, and edge cases," says Srikumar Ramanathan, chief solutions officer at MPhasis. "Validation must be layered, encompassing accuracy and compliance checks, bias and ethics audits, and drift detection using golden datasets.""

""AI agents are stochastic systems, and traditional testing methods based on well-defined test plans and tools that verify fixed outputs are not effective," says Nirmal Mukhi, VP and head of engineering at ASAPP. "Realistic simulation involves modeling various customer profiles, each with a distinct personality, knowledge they may possess, and a set of goals around what they actually want to achieve d"

"As more companies evaluate AI agent development tools and consider the risks of rapidly deploying AI agents, more devops teams will need to consider how to automate the testing of AI agents. IT and security leaders will seek testing plans to determine release-readiness and avoid the risks of deploying rogue AI agents. One best practice is to model AI agents' role, workflows, and the user goals they are intended to achieve. Developing end-user personas and evaluating whether AI agents meet their objectives can inform the testing of human-AI collaborative workflows and decision-making scenarios."

Testing AI agents should be treated as an enterprise risk management function covering architecture, development, offline testing, and production observability. Validation must be layered with accuracy and compliance checks, bias and ethics audits, and drift detection using golden datasets. Model agent roles, workflows, and end-user personas to evaluate human-AI collaborative workflows and decision-making scenarios. Use realistic simulation and digital twins to stress test agents against bad data, adversarial inputs, and edge cases. Implement continuous testing, automated release-readiness plans, and monitoring to detect stochastic behaviors and prevent rogue agent deployments.

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