Agents
An Analysis of Agents
In the Crowe framework, agents are autonomous, modular entities capable of executing tasks by leveraging large language models (LLMs), specialized tools, and persistent memory systems. This guide offers a detailed conceptual overview of how an agent functions — breaking down the stages from task reception to final output, and showing how its internal components work together.
Agent Components Overview
LLM (Large Language Model) — The reasoning engine that interprets instructions, understands context, and generates natural language or structured output.
Tools — External functions or integrations the agent can invoke to perform actions, such as querying databases, processing data, or calling APIs.
Long-Term Memory — Persistent storage systems (e.g., ChromaDB, Pinecone) that allow agents to retain and recall context from past interactions.
Agent Workflow
An agent’s workflow can be viewed as five main stages:
1. Task Initiation
Input: The task or query to be addressed.
Process: The agent formulates an initial plan for execution.
Output: A structured action plan or approach.
2. Initial LLM Processing
Input: The received task.
Process: The LLM interprets intent, requirements, and context.
Output: A preliminary response or action outline.
3. Tool Execution
Input: The action plan or specific subtasks.
Process: The agent calls one or more tools to gather data, run computations, or interact with external systems.
Function Calling: Tools are invoked as callable functions with well-defined inputs and outputs.
Output: Data or intermediate results from the tools.
4. Memory Interaction
Input: Results and contextual information from tools.
Process: The agent writes relevant information to long-term memory and retrieves past knowledge to enrich the current task.
Example Systems: Retrieval-Augmented Generation (RAG) using ChromaDB or Pinecone for historical context.
Output: Enhanced context and knowledge for the final stage.
5. Final LLM Processing
Input: Consolidated data and context from tools and memory.
Process: The LLM produces the final response or completes the action.
Output: The final answer, decision, or operation.
Agent Architecture & Workflow
Conclusion
Agents in the Crowe framework are versatile, high-performance entities that integrate LLMs, specialized tools, and persistent memory systems to execute complex tasks with precision. Through function calling for tool execution and RAG-enabled memory systems such as ChromaDB and Pinecone, they extend their reasoning capabilities, access historical context, and deliver outputs that are both accurate and contextually relevant.
This conceptual overview offers a clear picture of the agent lifecycle in Crowe — from task initiation to final output — providing a foundation for building advanced, collaborative multi-agent solutions capable of operating at enterprise scale.
Last updated