# Building Your Own AI Agent

DevFeed: [Building Your Own AI Agent](<https://devfeed.tech/articles/building-your-own-ai-agent-30787.md>)

Original publisher: [Read original article](<https://devblog.kogan.com/blog/building-your-own-ai-agent-from-consumer-to-producer>)

Author: Mark Elsden

Published: 2025-09-23T05:18:42Z

Content type: tutorial

Language: en

Sources: [Kogan.com](<https://devfeed.tech/sources/kogan-com.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Automation](<https://devfeed.tech/topics/automation.md>), [Generative AI](<https://devfeed.tech/topics/generative-ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Code](<https://devfeed.tech/topics/code.md>), [Development](<https://devfeed.tech/topics/development.md>), [Claude](<https://devfeed.tech/topics/claude.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [automation](<https://devfeed.tech/tags/automation.md>), [code](<https://devfeed.tech/tags/code.md>), [development](<https://devfeed.tech/tags/development.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [llm](<https://devfeed.tech/tags/llm.md>)

## AI overview

This developer tutorial explains the basic structure of AI agents and distinguishes generative AI models from agentic systems. It describes agents as systems that use language models to reason, plan, and call tools in an inferencing loop, and encourages engineers to use agentic AI for automation such as data pipelines.

## Source excerpt

The DEBI (Data Engineering and Business Intelligence) team recently attended the DataEngBytes 2025 conference, where the hot topic for the year was, unsurprisingly, AI agents. My favorite talk, by Geoffrey Huntley, presented a powerful and surprisingly simple idea: It's not that hard to build an agent; it's a few hundred lines of mainly boilerplate code running in a loop with LLM tokens. That's all it is! Kogan DEBI team at the DataEngBytes conference 2025 The speaker's main point was that things were developing extremely rapidly in the AI space, but rather than worrying about how AI might take engineering jobs in the near future, we should become AI producers, leveraging agentic AI to automate things, from data pipelines to our own job functions. Understanding this is, in his words, "perhaps some of the best personal development you can do this year." This idea is both liberating and empowering. It transforms the conversation from one of anxiety about job security to one of excitement about a new, fundamental skill. Let's pull back the hood on how these agents work and understand the simple primitives that allow us to become producers of automation, not just consumers. The Fundamentals: The Shift from a Tool to a System Before you write any code, you need to understand the new paradigm. We're moving beyond just using Generative AI (Gen AI) as a tool and are now using it to build a complete system: an AI Agent. Generative AI (Gen AI): The Creator: This is the broad category of AI models that are designed to create new content. LLMs are the most common form of this. They are reactive; you give them a prompt, and they generate a response--be it text, code, or an image. Gen AI is the creative engine. Agentic AI: The Doer: This is a type of AI system that is designed to act with autonomy. You give it a high-level goal, and it uses its "brain" (a Gen AI model) to reason, plan, and execute actions to achieve that goal. This is the proactive part of AI. The speaker referred