How AI agents use tools to reach APIs and databases, and where the risks lie

A language model alone can only generate text, so tool use is what lets it check live data, query records, or send messages. Under the hood, an agent is a simple loop in which the model requests a structured function call, the application executes it with its own credentials, and the result returns as an observation for the next step. Tool descriptions strongly shape whether the model picks the right tool, and the application must validate every call and keep API keys out of the model's reach. Database access raises the stakes, so the article recommends enforcing least privilege outside the model through read-only roles, allow-lists, timeouts, and confirmation for destructive writes. Benchmark results show models still make errors in multi-turn settings, and prompt injection remains a leading security concern for these systems.