AI agents can do a lot. When you mix a language model with some real-world tools—APIs, scripts, function calls—you get something that can reason, act, and respond in a way that starts to feel useful. These agents aren't just chatbots. They can look things up, call functions, and string together steps to solve complicated problems.
Sometimes, a single agent is enough. You feed it an instruction, it thinks, it acts, maybe calls a few tools in sequence. This is what's often called prompt chaining—one step feeds into the next. Simple enough.
So why introduce multiple agents? Why not just chain prompts forever in the same one?
I resisted that question for a while. If you swap out the prompt or tweak the tools, isn't that just creating a new "mode" of the same agent? What really makes one agent different from another? Is it the prompt? The tools? The LLM? If I fork an agent and each version remembers a different set of events, are they still the same agent? Or have we wandered into Ship of Theseus territory—one plank replaced at a time until it's something new? Or maybe it's more like the show Severance—same body, split minds.
But that line of thought, while fun, doesn't help you build systems. So let's reframe the question:
What's a practical way to tell when you need multiple agents, instead of one?
Here are a few reasons that make the case for going multi:
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Specialization. An agent with too many tools and too much context can get confused. Sometimes less is more. If you want reliable performance, it helps to give an agent a clear role and keep its toolbox small.
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Modularity. From a developer's point of view, it's just easier. You can build, evalutate, and deploy agents in isolation. One breaks, the others still work. One improves, the others don't need to change.
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Selective deployment. Maybe one agent is allowed to answer financial queries, but not access internal logs. Maybe another only talks to your backend, not your users. You can draw boundaries. You can decide who gets to do what.
So yes, multi-agent AI might grow out of prompt chaining. But at some point, it becomes something else—something more flexible, more organized, and more scalable.
And in that shift, it stops being just clever prompt design. It starts to look like software architecture.
Check out our other blog post: The Real Key to AI-Driven Data Engineering - It's Not What AI Does, But What It Doesn't Do where we explore what makes AI for data engineering successful.
Also read: Security at Yorph: What We Keep, What We Don't, and Why That Matters where we dive into our security-first approach to AI-powered data engineering.