AI Workflows vs. AI Agents vs. Everything in between

There are (again) a lot of words being thrown around in the AI world. Workflow automation, AI automation, AI agents, agentic workflows – and they all mean different things to different people.

For some, "AI automation" is just a workflow with an LLM in the middle. For others, it’s a fully autonomous system. And some companies (looking at you, Microsoft!) call anything that touches AI an "agent".

But when definitions get fuzzy, projects tend to get overbuilt, underbuilt, or simply set up the wrong way.

So this guide tries to break down the core approaches, show where each shines or fails, and help you choose the right one for your case.

Let’s dive in!

AI, Agents, Workflows – Why Even Care?

If you read my piece Your AI Agent Probably Should Be a Workflow, you already know how easy it is to overcomplicate AI systems. The problem gets worse when every vendor, framework, and platform uses different terminology for the same (or completely different) concepts.

As a technical or non-technical leader, this matters because the architecture you choose determines how reliable, maintainable, and scalable your AI solution becomes. If you ask for an "agent", you might actually get an agent – even when you don’t need one. It’s like choosing a monster truck (because it looks cool) to get to the bakery next door instead of a bike. Sure, both count as "vehicles", but only one makes sense for most.

That’s why getting a basic grasp of these architectures helps you avoid both extremes: overbuilding something you don’t need, or trying to solve modern AI problems with outdated logic.

With that foundation in place, let’s break down the main workflow and agent types and when to use which.

1. Workflow Automation (No AI)

“A sequence of predefined steps that can run automatically.”

This is the simplest (and still the most common) automation pattern. No intelligence, no dynamic decisions. Just a predefined sequence triggered by an event.

Think of it as classic automation:
A → B → C → done.

How It Works

• A trigger fires (form submission, CRM event, file upload).

• A fixed set of steps executes in the same order every time.

• Any “logic” is handled through simple if/else branches inside the workflow.

• The system is fully deterministic: the same input always yields the same output.

Examples

• New CRM lead → add to mailing list → notify sales

• Form submitted → create invoice → send confirmation email

• Daily ETL → clean data → update dashboard

When to Use It

• The process rarely changes

• Decisions can be made with simple rules

• The task is repetitive and predictable

If your workflow reads like a checklist, you don’t need AI. This approach is reliable, cheap, and easy to maintain.

When Not to Use It

• There’s no stable sequence of predefined steps

• Inputs vary significantly or require interpretation

• The task benefits from creativity, nuance, or synthesis

• Exceptions and edge cases are common

🚩 Red flag: If you’re piling on more if/else logic just to keep the workflow functioning, you’ve outgrown pure automation.

2. Automated AI Workflow

“A sequence of predefined, automated steps that utilize AI to achieve a certain outcome.”

This is where automation starts getting smarter—while trading a bit of determinism for flexibility. You still have a controllable linear (or branching) workflow, but one or more steps use AI to interpret content, classify inputs, or generate something.

Think of it as classic automation enhanced with intelligence.

A → AI Step → B → C → done.

How It Works

• The workflow still runs in a fixed, predefined order.

• AI appears as a step, not the orchestrator.

• An LLM might summarize, clean text, extract fields, categorize content, or generate drafts.

• A classical ML model might predict a number (e.g., forecast revenue) or classify a data point (e.g., lead scoring).

• The rest of the workflow handles routing, formatting, storing, or triggering downstream steps.

Your workflow gets smarter by utilizing AI as a part in it – without losing predictability over the overall process. And you can get very far with this pattern.

Examples

• User email → LLM categorizes issue → route to support team

• Customer note → LLM categorizes → LLM summarizes → save to CRM

• CRM record list → LLM drafts emails → store as Outlook drafts

• Uploaded document → LLM extracts fields → populate database

• Website form entry → ML model scores lead → notify sales

• Sensor measurement → ML model predicts quality → send alert

When to Use It

• You need interpretation, classification, or generation inside a predictable workflow

• Inputs vary, but the process doesn’t

• The order of steps matters and must be controlled

• You want clear human-in-the-loop checkpoints

This is the most common architecture for real business applications today.

When Not to Use It

• A simple rule-based workflow solves the problem more reliably

• You expect the system to figure out the path autonomously

• The process is non-linear or heavily dependent on open-ended reasoning

• Interactivity is required (e.g., dynamically requesting more input mid-run)

🚩 Red flags: You’re encoding simple rules inside the prompt (“do this if value > X”) or you need so many AI steps that the workflow becomes a chain of if/else logic disguised as prompts.

3. AI Agent

“An AI system that decides autonomously which steps to take to reach the goal.”

This is where we move beyond predefined workflows. Instead of following a fixed sequence, an AI agent takes a goal, reasons about what to do, and chooses actions or tools dynamically. Which is the very definition of an AI agent. Think of it as an LLM selecting steps (tools) in a loop to achieve a goal.

In non-technical words: Tell it the destination and let it figure out the route.

How It Works

• You provide a goal, not a step-by-step recipe.

• The agent analyzes the situation and chooses actions on the fly.

• It may call tools, query data sources, or run subtasks in any order.

• Planning, reasoning, and execution happen inside the agent loop.

• Each run may follow a different path depending on the context.

This introduces real autonomy – and real unpredictability. The lack of determinism is both the blessing and the curse of AI agents, because controlling them is no easy feat.

Examples

• Research agent → searches the web → reads pages → extracts insights → compiles a report

• Data cleanup agent → inspects dataset → identifies issues → chooses transformations

• Customer service agent → reads ticket → decides whether to answer, escalate, or request clarification and then performs the action.

• Systems agent → monitors logs → diagnoses issues → initiates remediation steps autonomously

When to Use It

• The system must choose between multiple possible actions

• The order of steps cannot be known upfront

• The task involves open-ended reasoning or exploration

• The workflow needs to adapt dynamically to new information

• Multiple tools or data sources might be needed depending on the case

An agent shines when the path to the outcome varies based on the information it uncovers along the way.

It’s powerful – but because of it’s high degree of integration and automation also the most challenging solution to build and maintain.

When Not to Use It

• A simple workflow (with or without AI steps) could solve the problem as well

• Predictability and deterministic behavior are required

• You need strict governance or audibility over each step

• The task doesn’t justify the added complexity, cost, and risk

🚩 Red flags: You rely on a specific sequence inside the agent’s system prompt (“Please remember to do step 2 before step 3!”) or you lack strong observability, guardrails, or evaluation metrics.

Agents thrive when they have autonomy within small, well-defined problem spaces. If you’re constantly trying to constrain an agent’s behavior, you probably didn’t need an agent in the first place.

4. Agentic Workflow Automation

“An AI agent embedded into an automated workflow.”

This architecture blends a predictable workflow with a focused slice of autonomy. Instead of letting an agent run the entire process, you place the agent inside a predefined workflow step. The workflow provides structure; the agent handles the part that requires dynamic reasoning or needs more flexibility.

Examples

• Claims processing → workflow collects documents → agent checks for missing info & decides what to request → workflow completes filing

• Content creation pipeline → workflow handles first draft → agent rewrites sections or improves structure → workflow checks output → workflow publishes

When to Use It

• Most of the workflow is stable, but one part needs dynamic reasoning

• You want autonomy in a contained, well-defined environment

• You need agent-like flexibility without giving up control of the overall process

5. Multi-Agent Systems (MAS)

“Multiple AI agents interacting in a shared environment.”

We’re already ~1,400 words in and reaching the limits of what works well in a single blog/newsletter. Multi-agent systems (MAS) deserve a full deep dive of their own (and I’ll probably write that soon). For now, just remember the key idea:

MAS are built from the exact same elements you’ve already seen – just arranged so multiple agents can work together.
The “trick” isn’t new architecture. It’s deciding how these agents interact.

How It Works

There are two common patterns:

1. Collaborative Agents (Peer-to-Peer)
Several agents cooperate inside the same workflow, but each agent focuses on a single outcome which gets passed to the others (for enhancement or review). The last agent produces the final output.

2. Hierarchical Agents (Manager–Worker)
One “manager” agent plans the work, breaks down tasks, and delegates subtasks to “worker” agents. The workers execute and report back, and the manager assembles the final result.

Examples

• Research project
  Planner agent defines tasks → researcher agent gathers information → critic agent evaluates → synthesis agent compiles insights (This is how tools like DeepResearch or Perplexity operate under the hood.)

• Coding systems
  Supervisor agent interprets a larger coding task → testing agent identifies failures → fixing agent attempts patches → reviewer agent checks code quality → supervisor assembles final code. (This is how tools like Cursor work)

As you can imagine, if managing a single agent is hard, managing a swarm of them isn’t exactly easier. If you’re not ready to control one agent, you’re definitely not ready to control many.

Bottom Line

Don’t get too hung up on the terminology. "Agent", "AI automation", "Agentic systems" – vendors will keep reinventing these words anyway.

What matters is understanding what you’re actually asking for.

A clear grasp of these architectures helps you avoid commanding a monster truck when all you need is a bike — or trying to solve modern AI problems with logic trees from 2012.

Know the tools, know their limits, and choose the simplest approach that delivers the outcome you want.

See you next Friday!
Tobias