1. Why AI Engineering: Chat vs workflow vs Agent

AI Chat vs. Workflows & Agents

Using chat interfaces is great for quick answers or simple tasks—like drafting emails, summarizing documents, brainstorming ideas, or analyzing short text.

But if you want to build scalable solutions with speed, precision, and automation, AI workflows and agents are essential. They let you handle complex data, automate multi-step processes, and even take action.

This series will walk you through that evolution: from single prompts → to structured workflows → to full-fledged AI agents. In this overview, I’ll start by breaking down the difference between AI chat, AI workflows, and AI agents.

Workflows vs. Agents

According to Anthropic: AI workflows are systems where LLMs and tools are orchestrated through predefined code paths. AI agents are systems where LLMs dynamically direct their own processes and tool usage—deciding how to accomplish the task on their own.

In simple terms:

Workflows follow a deterministic path—this means some one has already decided what steps to take. And exactly what the AI should help you do. This path is predefined, consistent and repeatable.

Agents follow a non-deterministic path—meaning the AI decides what steps to take as they go, which can vary each time based on the context.

How it differs in practice

Let’s say we want to create a competitive report.

Using ChatGPT: You could ask for a summary of competitors in a specific industry. It would pull from its internal knowledge and maybe surface-level internet data. You’d then manually copy that into a report.

Using a Workflow: You could build a system that first checks your internal database for past reports, then pulls in insights from customer interview transcripts, and finally formats everything into a standardized report.

Using an Agent: You’d just say, “Generate a competitive report for [X industry]. The agent would decide which data sources to access—maybe the internal reports and transcripts, but also live APIs, market research portals, or even visit competitor websites for the latest information. It chooses the steps, tools, and flow dynamically.

When to Use What

Workflows are best when you need reliability, consistency, and visibility into the process—like financial reporting or regulated environments. They are more constrained on the types of input they take and don’t do well with edge cases.

Agents shine when you need flexibility and speed , and care more about the output than how it was produced—think creative development, research, or ideation.

What Is AI Engineering—and Why Does It Matter Here?

AI engineering is the discipline of designing, building, and deploying systems that use AI—especially large language models—to solve real-world problems. It’s not just about prompting a model. It’s about turning AI into production-ready systems.

That includes:

Using chat interfaces for one-off answers or internal tooling

Building workflows with well-defined, code-based steps using tools like LangChain, LlamaIndex, or Python pipelines

Deploying agents that reason, choose tools, and operate independently using frameworks like CrewAI, AutoGen, or custom agent stacks

AI engineering is what turns LLMs into actual products—whether it’s an internal automation tool, a research assistant, or an intelligent chatbot that adapts to users in real time.

If prompting is the starting point, AI engineering is how you scale.