Dr. Violet Howard argues that AI agents are overused for deterministic processes. Linear workflows that follow fixed steps are more reliable, cheaper, easier to debug, and more scalable than letting agents dynamically choose tools.
This is what every single person is doing wrong when they're building AI agents. AI agents are great and there's definitely a time and a place for them because they have different tools and basically the agent will use its brain to understand, okay, I have these 3 tools based on what the user's asking me. Do I call this one and then do I output, or do I call this one then this one, or do I need to call all 3 simultaneously? It has that option and it has the variability there. So this is going to be a non-deterministic workflow. But the reality is most of the processes that we're trying to enhance for our clients are pretty deterministic workflows that we can build out with something more linear where we still have the same tools, we're still using AI, but we have everything going step 1, step 2, step 3, step 4, step 5, step 6, which is going to reduce the variability there. It's gonna be very deterministic and it's gonna help us with a lot of things. But real quick, let's talk about the pros of AI workflows over AI agents, and I narrowed it down to 4 main points. The first one is reliability and consistency. One of the most important concepts of building an effective AI agent is the system prompt, because it has to understand what its tools are, when to use each one, and what the end goal is. And it's on its own to figure out which ones do I use in order to provide that output. But with a workflow, we're basically keeping it on track, and there's no way that the process can sort of deviate from the guardrails that we've set up because it has to happen in order and it can't really go anywhere else. So the next one is going to be cost efficiency. When we're using an agent and it has different tools, Every time it hits a tool, it's gonna go back to its brain, it's gonna rerun through its system prompt, and it's gonna think about what is my next step here. And every time you're accessing that AI agent's brain, it costs you money. So if we're able to eliminate that aspect of decision making and just say, okay, you, you finished step 2, now you have to go onto step 3. There's no decision to be made. We don't have to make that extra API call to think about what comes next, and we're saving money. Number 3 is easier debugging and maintenance. When we have an AI workflow, we can see exactly which node errors, we can see exactly what mappings are incorrect and what happened here. Whereas with an AI agent workflow, it's a little bit tougher because there's a lot of manipulating the system prompts and messing with different tool configurations. And like I said, there's data flowing between agent to tool or between agent to sub-workflow. And that's where a lot of things can happen that you don't really have full visibility into. And then the final one is scalability, kind of backpacks right off of number 3. but if you wanted to add more nodes and more functionality to a workflow, it's as simple as, you know, plugging in a few more blocks here and there or adding onto the back. But when you want to increase the functionality of an AI agent, you're probably gonna have to give it more tools. And when you give it more tools, you're going to have to refine and add more lines to the system prompt, which could work great initially, but then previous functionality, the first couple tools you added, those might stop working or those may become less consistent. If you wanna see the full video, the link for that will be in the description. I'll see you guys over there.
