Acceptance testing is a testing method that verifies whether developed features meet business requirements and user stories, from the perspective of the product owner and stakeholders.
## "Working" and "Usable" Are Different While unit tests and functional tests verify "whether the code works correctly," acceptance tests verify "whether the product meets what the business requires." Even if there are no bugs, a release cannot proceed if the behavior differs from the requirements. Acceptance tests are often written in a format close to natural language. Scenarios are described in terms of user actions and expected outcomes — for example, "When an administrator logs in and opens the employee list, only employees belonging to their own tenant are displayed." The Gherkin (Given-When-Then) syntax is a representative format for this. ## Degree of Automation Some acceptance tests are executed manually, while others are automated using tools such as Playwright. In ATDD (Acceptance Test-Driven Development), acceptance criteria are defined first, implemented as automated tests, and then development begins. Since relying on manual testing tends to reduce execution frequency, automating critical scenarios has become standard practice. ## Relationship with Sprint Reviews In Scrum development, the results of acceptance tests are often reviewed during the Sprint Review. Since these results serve as the basis for the Product Owner to determine "whether this feature is acceptable," it is ideal for the team to agree on test scenarios during Sprint Planning.


A2A (Agent-to-Agent Protocol) is a communication protocol that enables different AI agents to perform capability discovery, task delegation, and state synchronization, published by Google in April 2025.

Agent Skills are reusable instruction sets defined to enable AI agents to perform specific tasks or areas of expertise, functioning as modular units that extend the capabilities of an agent.

Agentic AI is a general term for AI systems that interpret goals and autonomously repeat the cycle of planning, executing, and verifying actions without requiring step-by-step human instruction.


Ambient AI refers to an AI system that is seamlessly embedded in the user's environment, continuously monitoring sensor data and events to proactively take action without requiring explicit instructions.