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You can have the most capable model, a well-engineered harness, and a solid product vision - and still have no idea if your agent is actually working. That's the problem evals solve. An evaluation ("eval") is a test for an AI system: give an AI an input, then apply grading logic to its output to measure success. Good evaluations help teams ship AI agents more confidently. Without them, it's easy to get stuck in reactive loops - catching issues only in production, where fixing

Believe it or not AI is here to stay. In 2025, we saw a huge spike of Agentic AI Application and AI Agents. Now the question arise is how secure this AI Agents and Applications are? I was reading a research paper by Santiago Díaz, Christoph Kern, Kara Olive from Google in which they presented Google’s Approach for Secure AI Agents. In the Introduction, Google did presented and shared the potential and risks associated with the AI Agents and did mentioned the need for Agent