On this article, you’ll study the important thing variations between AI workflows and brokers, and how one can resolve which method is true to your use case earlier than writing a single line of code.
Subjects we are going to cowl embody:
- What distinguishes a workflow from an agent, utilizing concrete examples of every.
- A sensible single take a look at to find out whether or not your utility genuinely requires an agent.
- A five-point guidelines to information your resolution earlier than constructing.
“Agent” has develop into probably the most overused phrases in AI.
A chatbot with three instruments will get known as an agent. A set document-processing pipeline will get known as an agent. A scheduled automation will get known as an agent. Typically, a genuinely autonomous system that plans, acts, observes outcomes, and modifications its technique can also be known as an agent.
Because of all this hype, folks typically depend on brokers even when their utility doesn’t really want one.
So, earlier than going additional, let’s briefly perceive what an agent is and what a workflow is.
What Is a Workflow?
A workflow, additionally known as a pipeline or chain, is a system the place the management move is fastened at design time.
The developer decides the sequence of steps, branches, cease situations, and different logic beforehand. You should still use an LLM for a number of steps, which makes it a hybrid system, however the general path is predetermined.
For instance, if you must course of buyer refunds, your workflow may seem like this:
You’ll be able to see that there are selections right here. There are LLMs and instruments as nicely.
However it’s nonetheless basically a workflow as a result of the doable paths are designed upfront. You could possibly draw the state diagram earlier than receiving the client request.
What Is an Agent?
An agent is a system the place the LLM itself decides what to do subsequent at runtime.
It receives a aim, has entry to instruments, and decides which device to name, in what order, and when to cease. It may backtrack, loop, or collect extra data relying on what it discovers.
In different phrases, the management move lives with the mannequin.
Let’s say you could have a manufacturing outage and need to reply this query:
Work out why checkout failures elevated within the final half-hour and produce a probable root trigger.
You might give the system instruments for querying logs and metrics, looking out error traces, and studying incident paperwork. However you can not reliably know beforehand what the proper sequence of actions needs to be.
For one incident, it would do:
|
Examine error price → examine current deployment → examine stack traces → determine failing database name → confirm database latency |
For an additional incident, it would do:
|
Examine error price → section failures by area → examine CDN standing → examine DNS errors → determine regional supplier outage |
Right here, the commentary after every motion determines what the system does subsequent. That’s what makes it agentic.
The Single Sensible Take a look at
Ask your self one query earlier than writing any code:
Are you able to draw a whole flowchart of the duty earlier than the LLM ever runs?
- If sure, and each main step and department might be listed with affordable confidence, construct a workflow.
- If the following step depends upon what the system discovers throughout execution — comparable to new knowledge, sudden device outcomes, or intermediate findings — you most likely want an agent.
This single take a look at can get rid of many pointless brokers.
If a reliable engineer can describe the method on a whiteboard utilizing an inexpensive variety of conditional steps, the additional flexibility of an agent is often not well worth the added complexity.
One frequent mistake is assuming:
Workflow = easy
Agent = refined
That’s not true.
A workflow can include a number of LLM calls, retrieval, device calls, retry logic, human approvals, and complex enterprise guidelines.
On the similar time, a quite simple system can nonetheless be agentic if the mannequin itself decides what occurs subsequent.
So, earlier than making a choice, undergo the guidelines under.
A Easy Guidelines Earlier than You Construct
1. Can I listing the most important steps and branches earlier than runtime?
Sure → Workflow
For instance, if you wish to extract data from a contract and put it aside to a database, the general steps are already identified.
Learn the contract, extract the fields, validate them, and save them.
You might use an LLM for extraction, however you do not want an agent to resolve what occurs subsequent.
2. Is the enter variability low sufficient {that a} resolution tree stays maintainable?
Sure → Workflow
If the inputs are open-ended and unpredictable, an agent might make extra sense.
For instance:
Assist me clear up this uncommon buyer difficulty.
It might be troublesome to create a set workflow for each doable difficulty. On this case, letting an agent resolve dynamically what data to assemble and what motion to take might be helpful.
3. Is the applying delicate to quantity, value, and latency?
Excessive quantity, tight finances, or low-latency necessities → Workflow
Brokers usually require extra reasoning and power calls, which suggests extra tokens, extra API calls, and extra latency.
If the duty is much less frequent and useful sufficient to justify exploring a number of potentialities — comparable to advanced analysis or investigation — an agent might make sense.
For prime-volume FAQs or routine duties, keep on with workflows.
4. Do I would like similar execution paths for audit or compliance?
Strict audit or compliance necessities → Workflow
For instance:
|
Confirm id → test credit score → apply coverage → approve/reject |
If each utility should undergo the identical documented checks, use a workflow.
If totally different investigation paths are acceptable so long as the ultimate result’s appropriate, an agent could also be appropriate.
5. Have I already tried a workflow with LLM judgment?
That is often one of the best place to start out.
For instance, in buyer assist:
|
Fastened workflow → classify difficulty with LLM → test coverage → LLM judges eligibility → course of refund |
If this works nicely, you most likely don’t want an agent.
Workflow + LLM judgment → do that earlier than transferring to a completely autonomous agent.
Closing Takeaway
Brokers are highly effective when the issue is genuinely open-ended.
For a lot of enterprise processes, nevertheless, a well-designed workflow with focused LLM calls is less complicated, cheaper, extra dependable, and simpler to keep up.
The sensible method is to start out constrained.
Draw the flowchart first. Construct the workflow. Measure the place it fails. Solely then resolve whether or not an agent is definitely required — and even then, it could solely be wanted for a bounded a part of the duty. For those who can draw the flowchart earlier than the LLM runs, begin with a workflow. If the move needs to be found whereas the system is operating, you most likely want an agent.
