- [Instructor] Let's talk about agent intelligence,
how to make your agent smarter.
In simple terms, this comes down to which model you choose
and how you led that reason.
A large language model is the core brain of your agent,
which orchestrates the interaction flow
and handles the tool calls.
In our n8n setup, the agent was powered by Gemini 2.5 Flash,
which is a modern, lightweight and fast model.
But there's a large spectrum.
There are larger models like Gemini 2.5 Pro, GPT 5,
or Claude 4.5 Sonnet that have far more parameters.
Choosing a bigger model is your first lever
for more intelligence.
Bigger models generally tend to handle
nuance and context better,
but they're slower and more expensive.
Smaller ones respond instantly,
but they may miss detail or make simpler assumptions.
The key is finding the right balance
that fits your use case.
The good thing is that in n8n,
you can try out different models easily.
Just switch the model node in your agent,
and you're all set.
Another lever you have is to adjust the reasoning time
if the model allows this.
This controls essentially
how long the model is allowed to think.
You can imagine it like giving the model
more mental scratch space.
It generates more tokens to explore different avenues
to solve a given problem
before arriving at the final answer.
More reasoning steps usually improve accuracy,
but at the cost of latency.
Less reasoning gives instant results,
but sometimes only at surface level.
Here's what that looks like inside n8n.
You can pick a model that supports different reasoning modes
like OpenAI's GPT 5 models.
In this case, you can choose between
low, medium, or high reasoning efforts,
depending on whether you need quick replies
or more reliable reasoning.
Note that n8n sometimes lags a little behind
in the customization
compared to the original model provider APIs.
Besides picking a different model
or adjusting the reasoning time,
advanced teams can also try to fine-tune a model.
That means training the model on your own examples,
internal tools, interaction flows, or tone.
This is very powerful, but also much more complex.
Usually a later step
once your base agent works reliably
and you have collected data to fine-tune on.
In the end it's about trade-offs.
You can increase intelligence by choosing a stronger model
or give it more time to think,
but every game needs more time
and creates higher inference costs.
The art is to find the sweet spot
where your agent is accurate enough
and fast enough to feel responsive,
and you'll need a few good iterations to figure this out.
My tip, start simple.
Use a small fast model like Gemini 2.5 Flash
to get things running,
and once your setup is stable and you can measure results,
try larger models or longer reasoning time
to see if accuracy improves.
And don't forget to experiment with different providers too.
Models like Claude 4.5 Haiku or GPT-5 mini
sometimes follow your custom instructions
or your specific tools better out of the box.
So test and compare.
That's how you'll find your sweet spot
between speed, cost, and reliability.