On this article, you’ll study the important thing variations between AI workflows and brokers, and easy methods to resolve which method is correct to your use case earlier than writing a single line of code.
Matters we are going to cowl embody:
- What distinguishes a workflow from an agent, utilizing concrete examples of every.
- A sensible single check to find out whether or not your utility genuinely requires an agent.
- A five-point guidelines to information your choice earlier than constructing.
“Agent” has grow to be probably the most 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. Typically, a genuinely autonomous system that plans, acts, observes outcomes, and adjustments 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 movement 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 total path is predetermined.
For instance, if you must course of buyer refunds, your workflow may seem like this:
You possibly can 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 potential paths are designed upfront. You possibly can 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 purpose, 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 movement lives with the mannequin.
Let’s say you’ve got 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.
You could give the system instruments for querying logs and metrics, looking out error traces, and studying incident paperwork. However you can’t reliably know beforehand what the proper sequence of actions must be.
For one incident, it would do:
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Verify error charge → examine latest deployment → examine stack traces → determine failing database name → confirm database latency |
For an additional incident, it would do:
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Verify error charge → phase failures by area → examine CDN standing → examine DNS errors → determine 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 could be listed with cheap confidence, construct a workflow.
- If the subsequent step depends upon what the system discovers throughout execution — akin to new information, sudden software outcomes, or intermediate findings — you in all probability want an agent.
This single check 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 normally not definitely worth the added complexity.
One widespread mistake is assuming:
Workflow = easy
Agent = refined
That’s not true.
A workflow can comprise a number of LLM calls, retrieval, software calls, retry logic, human approvals, and sophisticated 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 call, undergo the guidelines under.
A Easy Guidelines Earlier than You Construct
1. Can I record the main 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 identified.
Learn the contract, extract the fields, validate them, and save them.
You could use an LLM for extraction, however you don’t want an agent to resolve what occurs subsequent.
2. Is the enter variability low sufficient {that a} choice tree stays maintainable?
Sure → Workflow
If the inputs are open-ended and unpredictable, an agent could make extra sense.
For instance:
Assist me remedy this uncommon buyer concern.
It might be troublesome to create a hard and fast workflow for each potential concern. On this case, letting an agent resolve dynamically what data to collect and what motion to take could be helpful.
3. Is the applying delicate to quantity, price, and latency?
Excessive quantity, tight funds, 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 helpful sufficient to justify exploring a number of potentialities — akin to complicated analysis or investigation — an agent could make sense.
For top-volume FAQs or routine duties, follow workflows.
4. Do I want similar execution paths for audit or compliance?
Strict audit or compliance necessities → Workflow
For instance:
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Confirm id → examine 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 appropriate, an agent could also be appropriate.
5. Have I already tried a workflow with LLM judgment?
That is normally the perfect place to start out.
For instance, in buyer help:
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Mounted workflow → classify concern with LLM → examine coverage → LLM judges eligibility → course of refund |
If this works effectively, you in all probability don’t want an agent.
Workflow + LLM judgment → do this earlier than shifting to a completely autonomous agent.
Ultimate 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 easier, cheaper, extra dependable, and simpler to take care of.
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 might solely be wanted for a bounded a part of the duty. When you can draw the flowchart earlier than the LLM runs, begin with a workflow. If the movement needs to be found whereas the system is working, you in all probability want an agent.
