Agent Architecture
In the Zytron framework, agents are designed to autonomously execute tasks by utilizing large language models (LLMs), integrated tools, and advanced long-term memory systems. This guide provides a comprehensive conceptual walkthrough of an agent's operation, detailing the sequence of actions taken to complete tasks and how internal components are leveraged effectively.
Components of Zytron Agents
Large Language Model (LLM):
The core component responsible for interpreting tasks, generating responses, and managing contextual understanding.
Tools:
External functions and APIs that the agent uses to perform specific operations, such as database queries or computational tasks.
Long-Term Memory Systems:
Advanced storage systems like ChromaDB or Pinecone, enabling agents to retain and retrieve information from past interactions, enhancing decision-making and contextual awareness.
Workflow of a Zytron Agent
The operation of a Zytron agent can be broken down into five primary stages:
Stage 1: Task Initiation
Input: A task or query provided by the user or system.
Process: The agent identifies the task requirements and prepares an initial plan.
Output: A structured approach or plan for task execution.
Stage 2: Initial LLM Processing
Input: The task or query.
Process: The LLM interprets the task, understanding its context and generating an initial response.
Output: A proposed action plan or structured response.
Stage 3: Tool Usage
Input: Specific sub-tasks or requirements identified by the LLM.
Process:
The agent invokes external tools to gather data, perform calculations, or interact with APIs.
Function Calling: Tools are utilized as functions with predefined parameters and outputs, ensuring precision in task execution.
Output: Results or data retrieved from the tools.
Stage 4: Memory Interaction
Input: Intermediate results and contextual information.
Process:
The agent interacts with long-term memory systems to store new data and retrieve relevant historical context.
RAG Systems (Retrieval-Augmented Generation): Systems like ChromaDB and Pinecone enhance task execution by integrating historical data into the current workflow.
Output: Enhanced context and refined data for further processing.
Stage 5: Final LLM Processing
Input: Comprehensive data and context derived from tools and memory systems.
Process: The LLM synthesizes the enriched data to produce a final response or execute the task.
Output: The task's completion or a final, actionable result.
Detailed Workflow Visualization with Diagrams
Agent Workflow Diagram
Workflow Summary:
The agent receives a task and interprets it through the LLM.
Relevant tools are invoked to perform specific actions.
Long-term memory is queried or updated as needed.
The final response is generated using enriched context and data.
Key Workflow Stages
Task Initiation: The agent identifies the task requirements and determines a logical approach.
LLM Processing: The task is analyzed for context and broken into actionable sub-tasks.
Tool Utilization: Functions and tools are employed to execute sub-tasks or retrieve necessary information.
Memory Interaction: Historical data and past interactions are leveraged to refine the output.
Final Processing: The LLM synthesizes all inputs and generates the task's final outcome.
Last updated