Tool Use & Integration

Function calling, API integration, tool selection, and error handling.

API Integration

API integration connects AI agents to external services via REST and GraphQL endpoints, handling authentication, rate limiting, pagination, and error recovery to enable real-world action.

Browser Automation

Browser automation enables agents to navigate websites, fill forms, click buttons, and extract information from web pages — turning the entire internet into a tool surface through either visual (screenshot) or structural (DOM) approaches.

Code Generation and Execution

Agents that write and execute code gain a universal tool — computation, data manipulation, visualization, and system interaction all become possible through generated programs run in sandboxed environments.

Dynamic Tool Creation

Dynamic tool creation enables agents to write, validate, and register new tools at runtime when existing tools are insufficient, turning the agent from a tool user into a tool maker that extends its own capabilities.

File and System Operations

File and system operations give agents the ability to read, write, search, and manage files and execute system commands — turning the local file system into the agent’s workspace like a desk where it can organize, review, and modify its materials.

Function Calling

Function calling enables LLMs to generate structured JSON objects that invoke external tools, bridging the gap between natural language understanding and programmatic action.

Model Context Protocol (MCP)

MCP is an open standard that provides a unified protocol for connecting AI models to external tools, data sources, and services through a client-server architecture with three core primitives: resources, tools, and prompts.

Structured Output for Actions

Structured output techniques — JSON mode, constrained decoding, and schema validation — ensure that LLM-generated actions conform to precise, machine-readable formats, eliminating the fragility of parsing free-text responses.

Tool Chaining

Tool chaining is the practice of using the output of one tool as the input to the next, enabling agents to complete complex tasks through sequential multi-step tool invocations that build toward a goal.

Tool Selection and Routing

Tool selection is the process by which an agent examines available tools, matches them against the user’s intent, and picks the right one to invoke — a decision that becomes harder as the tool catalog grows.