Agents

Agents are large language models (LLMs) that use tools in a loop to accomplish tasks.

These components work together:

  • LLMs process input and decide the next action
  • Tools extend capabilities beyond text generation (reading files, calling APIs, writing to databases)
  • Loop orchestrates execution through:
    • Context management - Maintaining conversation history and deciding what the model sees (input) at each step
    • Stopping conditions - Determining when the loop (task) is complete

Agent State and Context

Agents often need server-side state that should not be placed directly in the prompt, such as tenant settings, request IDs, feature flags, credentials, or progress through a task.

Use runtimeContext as the agent's shared runtime state. It flows through the agent loop, is available in prepareStep and lifecycle callbacks, and can be updated between steps. Use toolsContext for per-tool values such as API keys or scoped permissions; each tool receives only its own typed context based on its contextSchema.

Learn more in Runtime and Tool Context.

ToolLoopAgent Class

The ToolLoopAgent class handles these three components. Here's an agent that uses multiple tools in a loop to accomplish a task:

import { ToolLoopAgent, tool } from 'ai';
import { z } from 'zod';


const weatherAgent = new ToolLoopAgent({
  model: "xai/grok-4.6",
  tools: {
    weather: tool({
      description: 'Get the weather in a location (in Fahrenheit)',
      inputSchema: z.object({
        location: z.string().describe('The location to get the weather for'),
      }),
      execute: async ({ location }) => ({
        location,
        temperature: 72 + Math.floor(Math.random() * 21) - 10,
      }),
    }),
    convertFahrenheitToCelsius: tool({
      description: 'Convert temperature from Fahrenheit to Celsius',
      inputSchema: z.object({
        temperature: z.number().describe('Temperature in Fahrenheit'),
      }),
      execute: async ({ temperature }) => {
        const celsius = Math.round((temperature - 32) * (5 / 9));
        return { celsius };
      },
    }),
  },
});


const result = await weatherAgent.generate({
  prompt: 'What is the weather in San Francisco in celsius?',
});


console.log(result.text); // agent's final answer
console.log(result.steps); // steps taken by the agent

The agent automatically:

  1. Calls the weather tool to get the temperature in Fahrenheit
  2. Calls convertFahrenheitToCelsius to convert it
  3. Generates a final text response with the result

The ToolLoopAgent handles the loop, context management, and stopping conditions.

Why Use the ToolLoopAgent?

The ToolLoopAgent is the recommended approach for building agents with the AI SDK because it:

  • Reduces boilerplate - Manages loops and message arrays
  • Improves reusability - Define once, use throughout your application
  • Simplifies maintenance - Single place to update agent configuration

For most use cases, start with the ToolLoopAgent. Use core functions (generateText, streamText) when you need explicit control over each step for complex structured workflows.

HarnessAgent Class

Use HarnessAgent when you want to run a preconfigured established harness, such as Claude Code, Codex, or Pi, instead of building the loop yourself around a language model. Harnesses are a separate abstraction from providers and models, but stream into AI SDK-compatible result and UI primitives.

Learn more in Harnesses and HarnessAgent.

The rest of this section focuses on ToolLoopAgent, which is the AI SDK agent class for building your own model-and-tools loop.

Terminal UI

Use @ai-sdk/tui to run a ToolLoopAgent in an interactive terminal UI. It is useful for local agent development, demos, and internal tools where you want prompt input, streamed responses, tool cards, reasoning sections, scrolling, and tool approval prompts without building a custom interface.

import { runAgentTUI } from '@ai-sdk/tui';


await runAgentTUI({
  title: 'Weather Agent',
  agent: weatherAgent,
});

Learn more in Terminal UI.

Structured Workflows

Agents are flexible and powerful, but non-deterministic. When you need reliable, repeatable outcomes with explicit control flow, use core functions with structured workflow patterns combining:

  • Conditional statements for explicit branching
  • Standard functions for reusable logic
  • Error handling for robustness
  • Explicit control flow for predictability

Explore workflow patterns to learn more about building structured, reliable systems.

Next Steps