"Agents" are the most hyped word in AI right now, and that hype is pushing teams to build autonomous, self-directing systems for problems a simple pipeline would solve better. Both approaches use LLMs; they differ in who's in control. Choosing correctly is one of the highest-leverage decisions in an AI feature — it determines your reliability, cost, and how much you'll debug at 2am.
The difference isn't intelligence; it's who decides the next step.
Workflows are predictable and cheap. Agents are flexible and open-ended, but harder to control, more expensive, and slower.
For the vast majority of product features, a deterministic workflow is the right choice. If you can describe the steps in advance — and usually you can — then hard-coding them gives you enormous advantages: Most "agent" demos are really a fixed three-step pipeline dressed up in autonomy. Just build the pipeline. A chain of well-defined LLM calls, orchestrated in TypeScript with validation between steps, handles an astonishing range of real features.
Reach for a true agent only when the problem has these properties:
Research assistants, complex data investigations, and open-ended coding tasks can justify agents. A support responder or a document summarizer almost never does.
You don't have to choose purely. The best-designed systems are mostly workflow with a small agentic core exactly where flexibility is needed. Structure the overall flow as deterministic steps, and let the model make bounded decisions only at the point that truly requires judgment — with guardrails around it.
Whatever you choose, constrain it:
Start with a workflow. Add autonomy only where a workflow provably can't do the job — and you'll ship something more reliable and far cheaper than the agent everyone told you to build. If you're weighing the two for a real feature, let's talk.
Originally published on the Doktouri Agency blog. We build web, mobile, SaaS, and AI products — let's talk.