AI Agent in Testing: Why Strong Results Come from Engineering Circuit, Not Prompt

The article describes an approach to building an AI agent for testing that does not rely on ideal input data or model-generated guesses. Instead, the agent collects available evidence, builds an explicit acceptance model, verifies it through multiple independent means (API, DB, UI), documents knowledge boundaries, and leaves the final decision to a human. The engineering circuit includes scenario skills, safety rules, project configuration, deterministic executors, and a chain of evidence. Key testing methodologies include risk-based testing (prioritizing critical modules), exploratory testing (working with incomplete requirements), acceptance criteria testing with an independent oracle (pre-formulated observable expectations), system/integration thinking (tracing state chains), and classification of boundaries (not penalizing a task for unfinished adjacent work). The article emphasizes that the quality of the agent's response is determined not by the model but by the verification circuit.