In this article, we will build a simple understanding of the following:
- What a model is
- Why a model needs tools
- What tools are
- How an agent uses tools
Model vs. ChatGPT
Before understanding agents, let's clarify the difference between a model and ChatGPT. Whatever question we type into ChatGPT is sent to a model behind the scenes, which generates the response. You can think of ChatGPT as a web or mobile application — an interface through which we interact with the underlying Model/LLM.
Once upon a time, site reliability engineering rested on a linear assumption: monitor more, detect early, and you’ll recover faster. The rise of alert fatigue makes modern SRE teams realize otherwise:
Ramadass's (2025) paper, Building an AI-Powered Observability Pipeline for Modern System Reliability, cited research that discovered that:
When teams first integrate large language models (LLMs) into their software platforms, the initial experience often feels surprisingly simple. A developer writes a few lines of code, sends a prompt to a model API, and receives a response that looks intelligent, contextual, and almost magical. A prototype can be built in days, sometimes hours, and the business quickly starts imagining how AI will transform customer support, automation, analytics, and decision-making.
This early success creates a dangerous assumption: that moving from a working AI prototype to a production-grade AI system is simply a matter of increasing traffic and adding more users.
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