AI Agent
Enable flows with an AI Agent that uses LLMs and integrated tools to perform tasks, solve problems, and deliver intelligent responses.
Component description
How the AI Agent component works
AI Agent Component
The AI Agent component is a versatile building block designed to act as an intelligent agent within an AI workflow. This agent leverages large language models (LLMs), can connect to external tools, and is configurable for a wide range of use-cases such as conversational AI, complex automation, and dynamic task execution.
What the Component Does
At its core, the AI Agent processes input prompts, optionally considers conversation history, and can utilize external tools to generate context-aware responses. Its capabilities can be tailored by specifying a backstory, role, and goal, allowing the agent to behave according to a specific persona or objective. The agent can also perform function calling, enabling it to interact programmatically with APIs or external systems through enabled tools.
Inputs
The component exposes several configurable inputs, allowing you to customize its behavior:
| Input Name | Type | Required | Description |
|---|---|---|---|
| Chat History | InMemoryChatMessageHistory | No | Provides past conversation context for generating more relevant responses. |
| Function Calling LLM | BaseChatModel | No | Specifies the LLM for function calling tasks. |
| LLM | BaseChatModel | No | The main language model used for text generation. |
| Tools | List of Tool | Yes | A set of tools the agent can use (e.g., calculators, APIs, databases). |
| Backstory | String (multiline) | No | Background information to guide the agent’s behavior and responses. |
| Goal | String (multiline) | No | The primary objective or mission of the agent. |
| Input | Message | No | The input prompt or message to process. |
| Max Execution Time | Integer | No | Limits the time (seconds) the agent can spend on a task (default: 10). |
| Max Iterations | Integer | No | Maximum number of thinking steps (default: 10). |
| Max RPM | Integer | No | Limits requests per minute (default: 100). |
| Role | String (multiline) | No | Defines the agent’s persona or responsibilities. |
| Cache | Boolean | No | Enables result caching for efficiency. |
Note: Only the Tools input is strictly required; all other settings are optional and provide additional customization.
Outputs
The AI Agent component provides two main outputs:
Message Output:
The primary response from the agent after processing the input and utilizing any tools or context as configured. This is typically a message or text response suitable for display or further processing.Agent Object:
The underlying agent instance, which can be used for advanced chaining, introspection, or further manipulation within your workflow.
Use Cases and Practical Utility
The AI Agent is useful in scenarios where you need a conversational AI or automated assistant that can:
- Maintain and reference chat history for coherent multi-turn conversations.
- Dynamically invoke external tools or APIs (e.g., calculators, search, databases) as part of its reasoning.
- Be tailored with a customized backstory, role, and goal for domain-specific applications (e.g., customer support, research assistants, task automation).
- Handle complex tasks that require multiple steps or iterations.
- Apply constraints such as execution time and rate limits for operational stability.
Example Scenarios
- Conversational Chatbot: Build a chatbot that remembers past conversations and can answer follow-up questions accurately.
- Automated Research Agent: Configure with tools like web search or document retrieval to answer complex queries.
- Custom Workflow Orchestrator: Use tools to automate business processes or interact with other systems based on user input.
Summary Table
| Feature | Description |
|---|---|
| Connects to Tools | Yes (required) |
| Supports LLMs | Yes |
| Function Calling | Yes |
| Customizable Persona | Yes (via Backstory, Role, Goal) |
| Maintains Chat History | Yes (optional) |
| Configurable Limits | Execution time, iterations, and RPM |
| Outputs | Message response, Agent object |
Why Use This Component?
The AI Agent component enables the rapid creation of sophisticated, context-aware, and tool-augmented agents within your AI workflows. Its flexibility and rich configuration options make it suitable for a broad spectrum of AI-powered automation, conversation, and decision-support systems.
Examples of flow templates using AI Agent component
To help you get started quickly, we have prepared several example flow templates that demonstrate how to use the AI Agent component effectively. These templates showcase different use cases and best practices, making it easier for you to understand and implement the component in your own projects.
Frequently asked questions
- What does the AI Agent component do?
The AI Agent component acts as an autonomous entity within a workflow, using language models and connected tools to understand instructions, make decisions, and generate intelligent outputs.
- What types of tools can the AI Agent use?
The AI Agent can integrate with a variety of external tools, enabling it to perform searches, data processing, API calls, and more as needed to fulfill its goals.
- Can I set specific goals or roles for the AI Agent?
Yes, you can provide a backstory, define goals, and specify a role for the agent to guide its behavior and responses in the flow.
- How does the AI Agent interact with other components?
The AI Agent receives input, processes it with the help of integrated tools and LLMs, and outputs intelligent messages to downstream components in the workflow.
- Is there a limit to the agent's execution time or iterations?
You can configure maximum execution time and iteration limits to ensure the agent completes tasks efficiently and stays within resource budgets.
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