What is the Difference between an AI Agent and AI Workflow?
TLDR: AI Agents are non-deterministic, using LLMs to think and choose actions based on context, while AI Workflows are deterministic, following predefined steps. Workflows offer reliability, cost efficiency, and easier debugging. Choose agents for flexible tasks and workflows for repeatable processes.
AI Agents are a buzzy topic as everyone from FAANG companies to rug merchants is implementing AI in their business. AI Agents are tools used to automate repetitive or undesirable business tasks by utilizing AI. A basic understanding of AI Agents is that they are an LLM paired with a system prompt that directs thinking with tools to accomplish tasks. This definition seems straightforward, but it shares many similarities with AI workflows. Let’s talk about the differences between AI Agents and AI Workflows.
What are Deterministic and Non-Deterministic Processes?
An easy way to categorize AI Agents and AI Workflows is to identify if they are deterministic or non-deterministic. A deterministic process is one in which there is no randomness or variability. Say we were taking the subway one stop to the grocery store. We can only get on and off the subway. There is no variation involved in making this trip.
It is deterministic because there is a clear start and end, and a single path between the points. A non-deterministic process is one in which there is a start and end, but more than one way to get between the points.
Let’s review the subway example. Say there are three stops now between our starting point and the closest stop for the grocery store. The most obvious path would be to get on at stop 1 and get off at stop 4. However, there are no constraints that prevent us from getting off at stop 2 or stop 3 and walking. We could even get off at stop 2, walk to stop 3, and ride to stop 4.
This is a non-deterministic process because the method by which the goal is achieved is variable. As we introduce factors like delays, overcrowding of tram cars, etc., some paths may become more desirable.
How does determinism factor into the differences between AI Agents and AI Workflows? Let’s take a look.
What are AI Agents?
AI Agents are seen as a breakthrough tool for business tasks because they “think.” What does thinking mean? For AI Agents, thinking is using context and input to determine the best actions and the order to perform them. This is done without clear directions for how to accomplish the task.
Say you have a virtual assistant (VA) AI Agent. They receive some trigger, you speaking into your phone, and have to perform tasks based on the input they receive.
Your VA AI Agent may schedule a meeting with a client, block off time on your calendar, send a follow-up email to a customer, or reconcile an inventory miscalculation for your warehouse. The AI Agent has access to many tools and is responsible for determining intent and action based on the input and context it receives.
You can see here that this process is non-deterministic. Sending a meeting invite is a lot different from auditing inventory, even if both processes follow the steps of receiving voice input and performing an action. This is why AI Agents are seen as non-deterministic. Even if the input and output are consistent across actions, there is variability in which and how the action is performed.
What are AI Workflows?
AI Workflows are more familiar to low-code and no-code business users. Previously, workflows were known as automations. Services like Make and Zapier have made codeless automations accessible and popular. While these services did not always have LLM powers, adding LLMs did not reinvent automation. It supercharged it.
With AI Workflows, the LLM is used as a tool in a process step rather than an orchestrator for the whole process. AI Workflows are linear with some branching that is predefined by the user. Take our virtual assistant from the last example. It could have all of the functionality the AI Agent has, but the setup would look completely different.
Here, the LLM determines intent from the voice input, then branches to the correct automation flow. Once the needed action is determined, it is a linear process from input processing until the action is completed.
This is why AI Workflows are deterministic. Sure, there will always be some variability in the LLM itself, but how the process is tackled and the number of steps necessary are fixed for AI Automations. You will always have 1 LLM step to process the voice input and assign intent. From that, you have X steps based on the number of steps set up for the given process flow.
What are the Benefits of Using Workflows Instead of Agents?
AI Agents seem to do all the heavy lifting for your company. Why would you want to use AI Workflows? What seems easier is not always better. Having repeatable and deterministic processes in which you know what is happening under the hood is invaluable as your processes change and grow.
There are 3 main reasons why you would choose AI Workflows over AI Agents:
- Reliability and consistency
- Cost efficiency
- Easier debugging and maintenance
The first reason plays into our idea of deterministic versus non-deterministic processes. Having an LLM choose a different adjective for your product description has less impact than changing the dosage of medicine for a patient. For many processes, you want to have reliable and consistent results.
AI Workflows provide this because they are deterministic. You largely know what to expect when you use them for a process. If you deep dive into LLMs, you may be familiar with model controls like temperature, but that is unimportant here.
The second reason for using AI Workflows is cost efficiency. AI Agents and AI Workflows are interesting engineering because they feel like a basket of APIs taped together. This API usage gets expensive because every API wants to charge you per action they perform.
With AI Workflows, you have a specific number of API calls you’ll make for every process. AI Agents can differ. Does the AI Agent like the output from an API call? No, they call the API again. Does it lose track of its context? Yes, make another call to your LLM provider to determine the next step. This variability can lead to excessive API calls to perform straightforward tasks.
Going with the theme of AI Agent variability, we reach our third reason: easier debugging and maintenance. What’s easier to figure out: an instruction manual of numbered steps, or a handful of crumpled instructions on multiple pieces of paper? This is a comparison of debugging AI Workflows and AI Agents.
AI Workflows are linear with logic branches based on intent. They are very easy to read and troubleshoot when an issue arises. Did your AI Workflow send an email instead of a calendar invite? Check the branch node for determining intent.
Where do you start with AI Agents? Much of it will be focused on refining your system prompt, but for a multi-step process, you will need to be explicit with your prompt and careful when debugging.
Working with AI Agents and AI Workflows
As with all tools, AI Agents and AI Workflows are useful in their own situations. Need an autonomous tool to perform the thinking and actions of a process with an ambiguous context? You can use an AI Agent. Do you have a repeatable process that could benefit from LLM for intent detection and content generation? Implement an AI Workflow.
Whether you’re looking to implement AI in an existing workflow, automate a process, or build an AI Agent to interact with customers and your team, I am here to help. Contact me here to tell me about your needs and schedule a call to get your business running more efficiently.
Key Takeaways
- AI Agents are non-deterministic, leveraging LLMs to determine actions dynamically, ideal for tasks with variable paths like virtual assistants.
- AI Workflows are deterministic, using LLMs in fixed, linear steps for reliable, repeatable automation like intent-based task branching.
- Workflows provide cost efficiency with predictable API calls, unlike agents which may trigger variable, potentially excessive calls.
- Debugging workflows is simpler due to their linear structure, while agents require complex prompt refinement for troubleshooting.
