Functional testing (feature testing) is a testing method that verifies system behavior in terms of specific features or use cases. It covers a broader scope than unit testing, confirming that multiple modules work together correctly.
## Between Unit Tests and E2E Tests Functional tests occupy the middle layer of the test pyramid. While unit tests guarantee correctness at the function level and E2E tests verify the behavior of the entire system, functional tests validate "the result of a single user action." For example, a functional test for "creating an employee" runs through the entire flow from invoking the server action, through validation, saving to the DB, and returning the response. However, launching a browser is not required; it is executed via HTTP request simulation or direct server action calls. ## The Boundaries of Functional Tests The scope of "a single feature" can easily become ambiguous, but the criterion is simple. One server action call or one API endpoint call corresponds to one functional test. Chains of multiple actions — such as "create department → create job type → create employee" — are classified as integration tests. ## Differences from Acceptance Tests Functional tests verify from a developer's perspective whether "the implementation conforms to the specification." Acceptance tests verify from a business perspective whether "the requirements are met." Even when validating the same operation, the viewpoint of verification and the level of granularity in description differ.


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.

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.

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.

What is Multi-Agent AI? From Design Patterns to Implementation and Operational Best Practices

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.