AI Agents & Workflows

LLMs

An LLM (Large Language Model) in AI is a powerful deep learning model trained on massive text datasets to understand, generate, and predict human-like language, functioning as a core component of Generative AI to power chatbots, summarize documents, translate languages, and create content by recognizing complex patterns and context in text. ChatGPT, Gemini and Claude are widely used applications built on / powered by LLMs.

Key Distinctions

• LLM (e.g., GPT-4): The foundational, core model trained on vast text data to understand and generate human-like text.
• ChatGPT: An application interface that uses the LLM as its brain, adding features like conversation history, safety filters, and the ability to use external tools (like browsing or code execution) to create a more capable AI assistant.

Examples of LLMS

• GPT-5/4/4o (OpenAI).
• Gemini 3 Pro (Google).
• Claude Opus 4.5 (Anthropic).
• Kimi-K2 Thinking (Moonshot AI)
• Qwen3-Max (Alibaba).
• DeepSeek-R1 (DeepSeek-AI)
• LLaMA 4 Scout (Meta).
• Command (Cohere).

Key Traits of LLMs

• Open Domain Knowledge: LLMs have access to a vast amount of general knowledge, but they are not trained on proprietary information e.g personal info or internal company data.
• Passive: LLMs are passive in the sense that they do not generate text on their own. Instead, they generate text in response to user prompts.
• Vast Training Data: LLMs are trained on vast amounts of text data, enabling them to understand and generate human-like text.
• Contextual Understanding: LLMs can understand the context of a given prompt and generate text that is relevant to the context.
• Flexibility: LLMs can be fine-tuned for specific tasks, allowing them to perform a wide range of natural language processing tasks.

AI Workflows

If a user tells an LLM: "Everytime I ask for a personal event, perform a search query on my calendar and fetch data from my calendar before responding", that is a simple AI Workflow.

workflow-diagram

Input

LLM

Output

Calendar

Key Traits of AI Workflows

• Control Logic: They can only follow pre-defined paths set by humans. The paths are called control logic e.g. If we ask a follow up question like "What will the weather be?" on said event day, the LLM will fail to respond because that is outside the scope of its workflow.

Pushing this example further, if we allow the LLM to access the weather via an API and use a text-to-audio model to speak the answer: No matter how many steps is added, it is still an AI workflow Note: The human is still the decision maker, there is no AI agent involvement

workflow-diagram

Input

LLM

Output

Calendar

Weather App

Server Architecture

Figure 1: Visualizing data flow across region boundaries.

Implementing Raft Consensus

One of the most popular algorithms for managing a replicated log is Raft. It's designed to be understandable. Here is how you might verify the status of a Raft node in a production cluster.

The output confirms that our node is currently the Leader for term 42. All entries up to index 1024 have been committed and applied to the state machine.