On this article, you’ll be taught the important thing variations between AI workflows and brokers, and the way to determine which method is true on your use case earlier than writing a single line of code.
Subjects we are going to cowl embrace:
- 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 turn into one of the overused phrases in AI.
A chatbot with three instruments will get referred to as an agent. A hard and fast document-processing pipeline will get referred to as an agent. A scheduled automation will get referred to as an agent. Generally, a genuinely autonomous system that plans, acts, observes outcomes, and modifications its technique can also be referred to as an agent.
Resulting from all this hype, folks typically depend on brokers even when their utility doesn’t really need 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 referred to as a pipeline or chain, is a system the place the management stream is fastened at design time.
The developer decides the sequence of steps, branches, cease circumstances, and different logic beforehand. You should still use an LLM for a number of steps, which makes it a hybrid system, however the total path is predetermined.
For instance, if it’s important to course of buyer refunds, your workflow would possibly appear to be this:
You may see that there are choices right here. There are LLMs and instruments as effectively.
However it’s nonetheless basically a workflow as a result of the attainable paths are designed upfront. You may draw the state diagram earlier than receiving the shopper 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 objective, has entry to instruments, and decides which software to name, in what order, and when to cease. It will probably backtrack, loop, or collect extra data relying on what it discovers.
In different phrases, the management stream lives with the mannequin.
Let’s say you will have a manufacturing outage and wish to reply this query:
Work out why checkout failures elevated within the final half-hour and produce a probable root trigger.
It’s possible you’ll 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 right sequence of actions must be.
For one incident, it’d do:
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Test error price → examine latest deployment → examine stack traces → establish failing database name → confirm database latency |
For an additional incident, it’d do:
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Test error price → phase failures by area → examine CDN standing → examine DNS errors → establish regional supplier outage |
Right here, the remark after every motion determines what the system does subsequent. That’s what makes it agentic.
The Single Sensible Check
Ask your self one query earlier than writing any code:
Are you able to draw an entire flowchart of the duty earlier than the LLM ever runs?
- If sure, and each main step and department will be listed with cheap confidence, construct a workflow.
- If the subsequent step depends upon what the system discovers throughout execution — akin to new knowledge, surprising software outcomes, or intermediate findings — you most likely want an agent.
This single take a look at can remove 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 normally not well worth the added complexity.
One widespread mistake is assuming:
Workflow = easy
Agent = refined
That isn’t true.
A workflow can include a number of LLM calls, retrieval, software calls, retry logic, human approvals, and complex enterprise guidelines.
On the identical 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 key steps and branches earlier than runtime?
Sure → Workflow
For instance, if you wish to extract data from a contract and reserve it to a database, the general steps are already recognized.
Learn the contract, extract the fields, validate them, and save them.
It’s possible you’ll use an LLM for extraction, however you do not want an agent to determine 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 subject.
It might be troublesome to create a hard and fast workflow for each attainable subject. On this case, letting an agent determine dynamically what data to assemble and what motion to take will 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 gear calls, which suggests extra tokens, extra API calls, and extra latency.
If the duty is much less frequent and beneficial sufficient to justify exploring a number of prospects — akin to complicated analysis or investigation — an agent might make sense.
For top-volume FAQs or routine duties, stick with workflows.
4. Do I would like equivalent execution paths for audit or compliance?
Strict audit or compliance necessities → Workflow
For instance:
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Confirm id → test credit score → apply coverage → approve/reject |
If each utility should undergo the identical documented checks, use a workflow.
If completely different investigation paths are acceptable so long as the ultimate result’s right, an agent could also be appropriate.
5. Have I already tried a workflow with LLM judgment?
That is normally the perfect place to begin.
For instance, in buyer assist:
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Mounted workflow → classify subject with LLM → test coverage → LLM judges eligibility → course of refund |
If this works effectively, 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, nonetheless, a well-designed workflow with focused LLM calls is less complicated, cheaper, extra dependable, and simpler to take care of.
The sensible method is to begin constrained.
Draw the flowchart first. Construct the workflow. Measure the place it fails. Solely then determine whether or not an agent is definitely required — and even then, it might solely be wanted for a bounded a part of the duty. In case you can draw the flowchart earlier than the LLM runs, begin with a workflow. If the stream needs to be found whereas the system is operating, you most likely want an agent.
