Agent, Tools, Knowledge Bases or Function Calling?

When integrating large language models (LLMs) into applications, it is important to understand the different mechanisms that enable decision-making, execution, and knowledge retrieval. While terms like agents, tools, function calling, and knowledge bases are often used interchangeably, they have distinct roles.

Beyond these fundamental concepts, features like retrieval-augmented generation (RAG), memory, fine-tuning, and managed knowledge bases in Amazon Bedrock, add new dimensions to how AI systems operate.

This article explores the core methods for integrating AI models and when to use each in real-world applications.

The Core Mechanisms: Agent, Tool, Function Calling, and Knowledge Bases

Each approach has advantages, depending on whether structured execution, external capabilities, long-term memory, or autonomous behavior is required, so lets get into it...

What Is an Agent?

An agent is an autonomous AI entity that takes actions based on observations, goals, and external stimuli. It often makes decisions iteratively using a reasoning engine such as a planner-executor architecture.

Characteristics of an Agent

• Autonomous: Operates independently and decides what actions to take.
• Goal-Oriented: Works toward achieving a specified objective.
• Stateful: Can remember past interactions and decisions.
• Multi-Step Reasoning: Executes multi-step workflows instead of just single API calls.
• Uses Tools and Functions: Calls external APIs, functions, or databases as part of its workflow.

Real-World Example

A finance AI assistant that tracks market trends, queries financial APIs, and suggests portfolio adjustments.

A software development AI agent that writes, tests, and iteratively improves code without human intervention.

What Is a Tool?

A tool is an external system, API, or capability that an agent (or LLM) can invoke to perform a specific function. Unlike an agent, a tool does not make decisions—it simply executes a predefined operation. Function calling allows an AI model to invoke predefined functions with structured input and output. Unlike an agent, function calling does not involve iterative reasoning—it simply executes a single function when needed.

Characteristics of a Tool

• Passive Execution: Does not decide when to run—an agent or model must call it.
• Predefined Functionality: Can be an API, database, search engine, or external script.
• Stateless: Does not remember previous calls—every execution is independent.

Real-World Example

• A stock market API that fetches financial data.
• A SQL query tool that retrieves user data from a database.
• A chatbot invokes get_weather(“Zurich”), retrieves {“temperature”: 5, “condition”: “cloudy”}, and formats a response.

What Is a Knowledge Base?

A knowledge base is a managed structured or unstructured data repository that allows AI models to retrieve relevant information in real time. Unlike function calling, which executes deterministic functions, a knowledge base provides flexible retrieval of facts, documents, and previous interactions.

Characteristics of a Knowledge Base

• Structured or Unstructured: Can store documents, facts, FAQs, or embeddings.
• Long-Term Memory: Enables AI models to retain and retrieve information over multiple interactions.
• Scalable: Supports high-volume knowledge retrieval for enterprise applications.

Real-World Example

• A legal AI assistant retrieves case law and previous rulings from a managed knowledge base.
• A customer support chatbot pulls relevant answers from an internal documentation database.
• An AI-powered search tool uses Amazon Bedrock with OpenSearch to provide contextual responses.

Comparing Agents, Tools, Function Calling, and Knowledge Bases

Choosing the Right Approach for AI Integration

Understanding the differences between agents, tools, function calling, and knowledge bases is crucial for building efficient, scalable, and intelligent AI systems. While function calling provides structured execution and external integrations, and knowledge bases help AI retain and retrieve long-term information.

The distinction between tools and function calling is increasingly blurred as AI systems evolve, especially with platforms like Amazon Bedrock and Anthropic Claude integrating both seamlessly. Whether you’re developing an AI-powered assistant, automation system, or enterprise search solution, choosing the right method depends on how much autonomy, reasoning, and real-time data access your application needs.

What’s next? If you’re working on AI applications, consider experimenting with different approaches to see what best fits your needs...