Agents vs Workflows in AI

“Agents” are everywhere right now. Many people talk about them like they will do everything for you. But in real products, the story is more mixed. Some agent ideas work well. Some are still messy. And many times a simpler system does the job better.

This blog explains what AI agents are, how they differ from workflows, why “consumer agents” are often overhyped, and what developers should focus on if they want to build useful agent systems.

What people mean when they say “agent”

The word “agent” is used in many ways. Some people call almost any AI system an agent, especially if it does more than one LLM call. But that makes the term almost meaningless. A more useful definition is this:

An agent is a system where the language model can decide how many steps it needs. It runs in a loop, takes actions, checks results, and keeps going until it reaches a solution. So instead of a fixed script like “step 1, step 2, step 3, done,” the model chooses:

• what to do next

• which tool to use

• when it is finished

• when it needs to try again

That “unknown number of steps” is the key idea.

What is a workflow then?

A workflow is more planned and more controlled.

In a workflow:

• you decide the steps ahead of time

• the model follows a straight path

• the number of steps is fixed

A workflow often looks like this:

1. Prompt A → output

2. Prompt B uses output from A → output

3. Prompt C uses output from B → final answer

It is predictable. You know what will happen. You can still build powerful systems with workflows, and many production systems are workflows because they are easier to control, test, and monitor.

A simple way to remember the difference

Here’s a plain way to think about it:

• Workflow: “Follow this recipe.”

• Agent: “Figure out the recipe as you go.”

Workflows are usually safer and easier. Agents are more flexible but harder to control.

What an agent prompt looks like in practice

A workflow prompt is usually focused and specific. It tells the model what to do in one step. An agent prompt is more open-ended. It usually includes:

• the user question

• a list of tools the model can use

• rules for how to use them

• a goal like “continue until you have the answer”

Example

• “Here is the task. You can search the web, read files, run code, and keep going until you finish. Show your final answer.”

That is the “agent loop” idea: the model is not just answering, it is choosing actions and repeating until it is satisfied.

Why agents often fail in real life

Agents are exciting, but they can be hard to get right. One reason is simple: agents need context. Humans have lots of background knowledge. Models don’t automatically have the exact context your tool system needs. If you don’t explain the environment clearly, the agent makes strange choices.

Tools are part of the prompt, not “extra”

Another practical issue: developers write beautiful prompts, then give the model bad tools.

For example:

• tool parameters named “A” and “B”

• no documentation

• unclear tool descriptions

• confusing outputs

A human engineer would struggle to use a tool like that. The model will struggle too.

The key idea is:

Tool descriptions are part of the prompt. If the tool is unclear, the model can’t use it well, even if your main prompt is great. If you want your agent to act like a capable worker, you must give it tools that look like tools a capable worker could understand.

When agents are useful

Agents shine in tasks that are:

• valuable

• complex

• hard to do in one pass

• able to benefit from repeated attempts

Two common examples are:

1) Research

Research often involves:

• searching

• reading many sources

• refining the search

• collecting evidence

• summarizing and filtering

It’s naturally iterative. Agents can loop and keep improving.

Also, research can handle “imperfect” results. You can collect extra info and filter later. That makes it a good fit for agent behavior.

2) Coding

Coding is special because it can be checked.

You can run:

• unit tests

• linters

• compilers

• build steps

This creates feedback. The agent can:

1. write code

2. run tests

3. see failures

4. fix code

5. run tests again

6. repeat until tests pass

That feedback loop helps the agent “converge” to a correct result.

The big word: verifiability

A repeated idea is verifiability. Agents work better when there is a clear way to check if they are right. In coding, checks are easier because tests can pass or fail.

In many other tasks, there is no clear check. If the agent is writing a marketing strategy or booking travel, “correct” is not obvious. That makes the loop noisy. Without a strong signal, the agent can wander.

A simple way to say it:

• If you can measure success, agents can improve with retries.

• If you can’t measure success, retries can just create more confusion.

Why “consumer agents” are overhyped

The transcript takes a strong stance here: consumer agents are often overrated right now. The reason is not that models are bad. It’s that consumer tasks are hard to fully specify. Take vacation booking:

To let an agent book a full vacation, you must explain:

• dates

• budget

• airline preferences

• flight times

• seat choices

• hotel style

• neighborhood preferences

• food needs

• risk tolerance

• refund rules

• what counts as “a good trip”

For many people, describing all of that clearly is almost as much work as booking the trip themselves. Then comes the bigger issue: verification. You don’t want an agent to buy plane tickets without approval. Mistakes are expensive. Checking everything the agent does becomes a burden. So even if the agent can do it, the cost of controlling and verifying it can kill the benefit.

Will consumer agents get better?

Yes, but it will take time.

One reason consumer agents may improve is context over time. If a system learns your preferences and stores them safely, you won’t have to explain everything from scratch.

But that requires:

• time to collect preferences

• good memory and personalization

• trust and safety controls

• clear approval steps

So the path to “book my whole vacation” likely involves smaller steps first.

What is underhyped: small time savers

A nice point in the transcript is that even tiny automations matter. If an agent saves you one minute, you might think:

“Who cares? It’s only a minute.”

But if you can automate that one-minute task, you might do it 100 times more than before. It changes the way people work. It unlocks scale.

So “small wins” can be huge, especially inside companies.

The real challenge: knowing when you need an agent

One of the hardest parts is choosing the right approach. Agents are not always the best solution. Sometimes a workflow is better. Sometimes a single LLM call is enough.

There is a “sweet spot” where agents make sense:

• the task is complex enough to need looping

• the task has measurable outcomes

• mistakes are not extremely costly

• you can monitor it without huge effort

That sweet spot is not always obvious. You often discover it by looking closely at real processes and testing.

What agents could look like in 2025

The transcript gives two ideas about the future:

1) Business adoption

We may see agents become common inside companies for repetitive tasks like:

• checking pull requests

• updating documentation

• filing tickets

• generating internal reports

• doing repeatable research tasks

Many of these are “cheap to verify” compared to consumer purchases.

2) Multi-agent systems (more experimental)

Multi-agent systems are where multiple agents work together, like a small team.

This can create interesting behavior and might become useful, but it’s still early. Even single-agent success in production is not fully solved everywhere, so multi-agent is more of a research direction today.

Best advice for developers building agents

1) Measure results

Don’t build in isolation. If you can’t measure whether it works, you won’t know if you are improving or wasting time.

You need:

• success metrics

• test cases

• logs

• evaluation methods

• feedback loops

2) Start simple

Many impressive products do a lot with one model call and strong surrounding code.

Simple systems are:

• easier to debug

• easier to control

• easier to improve

• more stable as models change

You can always add complexity later.

3) Build systems that age well

Good orchestration and good structure keep working even as the model improves. That’s future-proofing: your system becomes better automatically when a better model arrives.

Final takeaway

Agents are not magic robots that replace all work. They are a design choice.

Use an agent when:

• you need repeated attempts

• the number of steps is unknown

• you can verify progress

• the cost of mistakes is manageable

Use a workflow when:

• the steps are known

• you want predictability

• you need strong control

• testing and monitoring must be simple

And no matter what you build, give the model good tools, clear instructions, and a way to measure success. That’s what separates fun demos from systems that actually work.