What is Tool Use in AI?

Tool use is the broader concept that function calling is a specific implementation of. When people talk about an 'agent', they almost always mean 'an LLM with tool use plus a loop' — the model decides which tool to use, executes it, observes the result, and decides what to do next. The tools an LLM can use fall into five categories. (1) APIs: HTTP calls to external services — send email, fetch weather, query a CRM, post to Slack. (2) Code execution: running Python, JavaScript, or shell commands in a sandboxed environment; useful for calculations, data analysis, and scripting. (3) File system access: reading and writing files, traversing directories — the primitive underneath coding agents like Cursor and Claude Code. (4) Web browsing: fetching web pages, clicking links, filling forms — can be via API (Perplexity-style) or via actual browser automation (BrowserBase, Playwright-driven agents). (5) Computer use: the most general form — controlling a full OS, clicking pixels, typing in any app — currently offered by Anthropic's Claude computer-use API and OpenAI's Operator. Implementation-wise, tool use relies on function calling as the transport. Every tool (regardless of what it does under the hood) is described to the model as a function with a JSON Schema. When the model wants to use the tool, it outputs a function call. Your runtime intercepts that call, executes the real tool, and returns the result. The model then continues reasoning with the new information. The loop — reason, call tool, observe, reason again — is what gives agents their iterative problem-solving ability. Tool use matters because it collapses two major limitations of raw LLMs. First, knowledge freshness: an LLM trained in 2024 doesn't know today's stock price, but a model with a web-search tool does. Second, action-taking: an LLM alone can only output text, but an LLM with tools can actually send the email, create the task, deploy the code. This is the unlock that turned 'AI assistants' from curiosities into productive workforce members. Quality of tool use varies dramatically across models. Claude 4.5 and GPT-5 handle complex multi-step tool-use scenarios well — they know when to use which tool, pass arguments correctly, and recover from errors. Smaller models often misuse tools (using file-write when file-read was appropriate, passing wrong arg types, getting stuck in retry loops). The cost pattern is important: tool use multiplies inference cost because each tool call is a full LLM round trip. A task with 20 tool calls costs roughly 20× a single-turn answer. Tycoon's AI employees are tool-use agents by construction. Astra (the AI CEO) has tools for assigning tasks, querying metrics, posting messages, reading memory, and invoking specialist agents. An AI CMO has tools for publishing to Ghost, checking Google Analytics, posting to social platforms via Composio. Every time an AI employee 'does' something rather than 'says' something, that's a tool use call underneath.