To improve the testability of Agentic AI Systems, we propose that agentic AI systems be built with internal instrumentation – hooks at key components (input and output prompts, tool/service/API calls, memory, etc.) that are active during development and testing, and can be toggled or closed in production. These built-in test interfaces enable both rigorous pre-deployment testing (e.g. unit tests for agent decision steps, adversarial red-teaming, robustness evaluations) and ongoing post-deployment monitoring (capturing behavioral traces for auditing and anomaly detection). By treating the AI agent not as an opaque structure but as a composition of testable sub-components, developers can pinpoint failure modes and ensure each part meets reliability and safety criteria. This approach supports behavioral traceability (recording the agent’s step-by-step reasoning), decision auditability (retaining logs of decisions and actions for later review), tool/function call transparency (monitoring external API calls or real-world actions), and other critical testing areas like robustness and safety via red teaming.