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Agentic AI isn’t like our typical software. It doesn’t just give the answers, it thinks, decides and acts. These systems are designed to pursue goals, make choices, and even change their behavior as they go. That’s a whole new level of complexity which we need to observe/monitor. So naturally, the old school testing methods we used for static outputs just don’t cut it anymore. We need to look beyond simple inputs and outputs and start evaluating how these AIs reason, adapt, and align with what we actually want them to do.

Testing Agentic AI is more like scenario planning than ticking the boxes. We are not just checking if the answer is correct, actually we are watching how it gets there. That means creating a real world simulations, throwing edge cases as it, and checking how it handles our long term goals. Tools like AutoGPT, LangGraph and OpenAI’s function calling are great, but they also add complexity by chaining multiple decisions and tools together. So our test strategy needs to watch for things like loop traps, drifting from original task, or misusing tools. Think of it like mentoring a smart but unpredictable intern, can they think on their feet and still follow the company’s values?

When it comes to Agentic AI, testing isn’t just about right or wrong anymore. It’s about understanding how and why it did and what it did. That means tracking its reasoning, watching how it explores different paths and seeing whether it learns or self corrects along the way. We also need new metrics like how well it achieved the goal, whether it decisions and made sense, and if we had to step in. As Agentic AI continues to evolve, testing has to evolve too. It’s not optional, it’s how we build confidence in what these systems are doing and why.