# Building an Agent with RAG, Tool Calling, Memory, and Feedback

DevFeed: [Building an Agent with RAG, Tool Calling, Memory, and Feedback](<https://devfeed.tech/articles/what-you-need-to-make-an-agent-37654.md>)

Original publisher: [Read original article](<https://swizec.com/blog/what-you-need-to-make-an-agent>)

Author: hi@swizec.com (Swizec Teller)

Published: 2026-05-05T00:00:00Z

Content type: opinion

Language: en

Sources: [Swizec Teller](<https://devfeed.tech/sources/swizec-teller.md>)

Topics: [Retrieval-Augmented Generation](<https://devfeed.tech/topics/retrieval-augmented-generation.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [agent](<https://devfeed.tech/tags/agent.md>), [memory](<https://devfeed.tech/tags/memory.md>), [rag](<https://devfeed.tech/tags/rag.md>)

## AI overview

The author outlines four components for an agent used in internal tooling and processes: retrieval-augmented generation (RAG), tool calling, memory, and a feedback loop. The article describes using tools to retrieve external data and returning results to an LLM's context, while noting that long prompts may require memory-management techniques.

## Source excerpt

I've been digging into agents for internal tooling and processes. Looking for how to scale without drowning in manual work. Here's what I found