#51 | Stop Building AI Agents When You Actually Need Workflows.

I’ve been watching the automation space closely lately - Upwork gigs, YouTube tutorials, Substack discussions. There’s a pattern I keep seeing, and it’s costing people time, money, and sanity.

We’re mixing up our terminology. Worse, we’re mixing up our solutions.

The moment someone drops an LLM call into their system, they call it an “AI agent.” Then they proceed to build something that would be better served as a simple workflow. I see this everywhere: in my day job, in freelance briefs, in product specs that cross my desk. The industry has collectively decided that if it touches a language model, it must be an agent.

AI agents and AI workflows are fundamentally different, and understanding this difference will save you from a world of pain.

An AI workflow is deterministic. You control the flow from A to Z programmatically, the old-school way. Sure, you’ve got LLM calls sprinkled throughout - maybe you’re using GPT-4 to classify customer emails, or Claude to extract data from PDFs - but you never hand over control to the model itself. The flow is yours. You decide what happens next, what gets called when, and where the data goes.

An AI agent, on the other hand, gives the LLM the wheel. The model decides what actions to take, which tools to use, what comes next in the sequence. And when you start building agent swarms - with an orchestrator node managing multiple specialized agents chatting between themselves - you’ve entered a realm of complexity that most people drastically underestimate.

Here’s what nobody tells you about building reliable AI agents: they’re expensive to build, harder to debug, and require constant maintenance.

When you give an LLM control over the flow, you introduce a level of non-determinism that cascades through your entire system. A slight variation in user input - a different phrasing, a cultural nuance, an unexpected language choice - can send your agent down an entirely different decision tree. Suddenly your agent is stuck in a loop between nodes for ten minutes, or it prematurely decides a task is complete, or it produces an output that’s technically correct but operationally useless.

Tracing becomes a nightmare. Based on the input prompt, you get a different set of decisions every time. The logic is dynamic, the call stack varies, and good luck writing comprehensive evaluation tests.

Sure, you can document your top use cases and test against those. But the missing piece - that edge case you didn’t anticipate - is exponentially harder to catch when the system itself is making routing decisions.

I watch people on Upwork post gigs for AI agents when what they actually need would take a fraction of the time to build as a workflow. And I get it - agents sound impressive. They’re sexy. They’re what everyone’s talking about. But impressive doesn’t mean appropriate.

AI workflows are boring. Predictable. Simple, even.

You map out your nodes in n8n, Make, or Zapier. You know exactly which pieces are deterministic and which ones interact with an LLM. You build in validation steps. You add post-LLM checks to verify outputs. You have logs, clear integration points, and when something breaks, you know exactly where and why.

Yes, it’s a dumbing down of the technology. Yes, it’s less impressive in pitch meetings. But here’s what it also is: maintainable, scalable, and reliable.

Think about most business processes. They’re Standard Operating Procedures - SOPs - dressed up in various forms. Customer support workflows. Data extraction pipelines. Content approval processes. These map beautifully to deterministic flows with AI components. They don’t need the AI to decide what comes next. They need the AI to handle specific, well-defined tasks within a larger, predictable structure.

You want the LLM to be a powerful component, not the architect of your system. The same way you wouldn’t let a machine learning model decide your database schema, you shouldn’t let an LLM control your business logic unless you absolutely need that level of autonomy.

Let’s talk about the elephant in the room: there’s a perverse economic incentive at play here.

AI agents are expensive to build. They’re time-consuming to debug. They require ongoing maintenance and refinement. For consultants and agencies, this means more billable hours, longer retainers, and clients who depend on you because the system is too complex for them to manage internally.

AI workflows, on the other hand, are straightforward. They take less time to build. They’re easier for clients to understand and eventually manage themselves. From a business perspective, they’re less lucrative to sell.

But here’s the thing: if you’re a product person, your job isn’t to maximize consulting hours. It’s to solve problems efficiently. And nine times out of ten, maybe even ninety-nine times out of a hundred, an AI workflow solves the problem better than an agent. At this point of time.

I’m not saying agents are never the answer. There are legitimate use cases where you need that level of autonomy and dynamic decision-making. Complex research tasks that require exploring unknown information spaces. Customer service scenarios where the problem space is too vast to map out deterministically. Creative workflows where the value comes from the AI’s ability to navigate ambiguity.

But these cases are the exception, not the rule. Maybe ten percent of what I see described in project briefs actually requires a true AI agent architecture.

The rest? They’re workflows wearing agent clothing.

Ask yourself these questions:

Can you map out the process from start to finish? If yes, you probably want a workflow.

Do you know what “good” looks like at each step? If yes, you probably want a workflow.

Is the problem space well-defined, even if some steps require AI assistance? If yes, you probably want a workflow.

Do you have the resources to dedicate significant ongoing engineering hours to maintenance and refinement? If no, you definitely want a workflow.

The only time you should reach for agents is when the problem genuinely requires autonomous decision-making across an unpredictable action space, and you have the budget and expertise to build it properly.

There’s pressure to reach for the shiniest tool. It’s human nature, and it’s amplified in tech where being cutting-edge often conflates with being competent.

But the best solutions are often the boring ones. The ones that work consistently. The ones you can explain to your stakeholders without a whiteboard and thirty minutes. The ones that don’t wake you up at 3 AM because they’ve entered some strange loop state.

AI workflows give you that reliability. They let you leverage language models where they excel - understanding natural language, extracting meaning, generating text - without surrendering control of your system to them.

Build your foundation with stable, reliable code. Add AI as powerful, contained components within that framework. Keep the windows, but make sure you’ve got solid walls and a good roof.

That’s not settling for less. That’s building something that actually works.

And in the long run, that’s worth more than all the impressive demos in the world.

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