Agent Framework and SDK
Explore the components and capabilities of the StateSet ReSponse AI Agent Framework and SDK.
StateSet ReSponse AI Agent Framework and SDK
The StateSet ReSponse AI Agent Framework is a powerful suite of tools and libraries designed to facilitate the development and deployment of sophisticated AI agents. This framework provides a robust foundation for building intelligent, autonomous agents that can handle complex tasks.
Framework Overview
The ReSponse AI Agent Framework integrates several key components to create a complete system for building intelligent agents.
Key Components:
- Application: The user-facing interface or system interacting with the AI agent.
- Embedding Model: Transforms text queries into vector embeddings for semantic search.
- Metadata Filter: Filters vector results based on specified metadata.
- API: Provides the interface for interacting with the Vector Database.
- Vector Database (VDB): Stores and retrieves vector embeddings, allowing for fast similarity searches.
- Context: Provides relevant information to the LLM for generating responses.
- LLM (Large Language Model): Generates responses based on the context and query.
- LLM Models: (GPT4o, Claude, Llama3) - The specific large language models that you can choose from.
- Knowledge Base: (Memories, Rules, Attributes, Examples) - Stores relevant information about the agent’s configuration.
- Workflows: (Actions 1-3) - Define the sequence of operations that an agent can perform.
- Agent: The core entity that leverages the LLM, Knowledge Base and Workflows.
- Scheduler: Manages the timing and triggering of scheduled messages.
- Macros/FAQs: Predefined sets of information and responses.
SDK Overview
The ReSponse AI Agent SDK is built on top of the StateSet Cloud platform infrastructure, which includes:
- A deterministic workflow engine
- An event-driven architecture
- A state-of-the-art AI model
Each Agent has its own configuration and set of modules that are used to define the behavior of the agent.
Modules
The SDK is organized around the following modules:
- Agents: Configure agent-specific settings and behaviors.
- Knowledge: Store and manage the agent’s knowledge base.
- Attributes: Define properties and characteristics of the agent.
- Rules: Establish logic and conditions for the agent’s actions.
- Functions: Add custom code and capabilities to the agent.
- Memories: Store and retrieve past interactions to maintain context.
- Examples: Provide training data for the agent to learn from.
- Schedules: Set up recurring tasks and notifications for the agent.
- Settings: Configure general settings and parameters for the agent.
Each module has its own set of endpoints that developers can use to build, customize, and deploy agents.
AI Agents
Robert is a customer experience agent built on the ResponseCX Platform.
Robert - AI CX Agent
Robert - CX Stats
Interaction Overview
Here’s an overview of how the different components interact:
- The Application sends a Query to the Embedding Model.
- The Embedding Model generates a vector embedding of the query.
- The Metadata Filter is used to filter the results.
- The filtered vector embedding is sent to the API for retrieval of information from the Vector Database.
- The API retrieves relevant vectors and corresponding data.
- The retrieved data is passed as a Context to the LLM.
- The LLM uses the Context and Knowledge to generate a response, which is then streamed back to the Application.
- The Agent triggers predefined Workflows, which are then controlled by the Scheduler.
This framework allows for the creation of a wide range of intelligent agents with varying capabilities and levels of autonomy.