- [Instructor] Now, let's look at how to observe your agent,
what it's doing when it succeeds and when it silently fails.
Monitoring and observability are what turn
an experimental workflow into something reliable
you can run in production.
If you can't see what your agent does, you can't improve it.
Let's set some terminology straight.
Monitoring shows what happens,
the runs, the errors, the timing.
Observability explains why it happens,
connecting those runs to the agent's reasoning and tool use.
And finally, evaluation checks
whether the outcome is correct.
Together these give you the full picture,
what happened, why, and if it worked as intended.
First, let's talk about monitoring.
You'll monitor your agent in two stages,
at once while building it,
catching tool call issues and logic errors early,
and then later in production, making sure it stays stable
and behaves predictably with real users.
Now, let's look at monitoring and development.
Here, monitoring usually means opening n8n's execution logs
and reading what happened.
Did the agent called the right tool?
Did it sent the right payload?
You're looking for patterns
where it misunderstands instructions
or passes wrong parameters.
And once your agent is live, you move to metrics.
How often does it fail? Which tools cause slowdowns?
You can hook n8n into monitoring dashboards,
send logs to a central store,
or add error alerts through Slack or email.
Now, let's talk about observability.
Here's a great example of observability in action.
You can see the agent's full workflow chain,
the chat message, the tool it called,
and the exact error it hit.
In this case, the calculator tool failed
because the parameter size_sqft was sent as text,
100 square feet, instead of a plain number.
And that's what observability gives you,
insight into the agent's inner loop, how it used the tool,
what inputs it passed, and where things went wrong.
With that visibility, you can fix the issue immediately
or add validation to prevent it in production.
Looking at evaluation,
evaluation is where you test your workflow
under controlled conditions
before or after deployment.
You want to see whether it produces the right results,
and this usually involves running
a test dataset through your agent.
Each test case includes a sample input
and usually the expected output.
That's how you measure
consistency and accuracy automatically.
Evaluations catch regressions before your users do.
They help you compare model versions or prompts objectively,
and they give you a measurable sense of improvement,
not just a gut feeling.
In the case of n8n,
it even includes a built-in evaluations feature.
This lets you define test cases right inside your workflow,
specify expected outputs, and verify results after each run.
You can explore that feature if you click
on the Evaluations tab at the top of your workflow canvas.
Evaluation is really what separates
the flaky proof of concept
from a stable production workflow.
Without a tight monitoring and evaluation loop,
your agent will never leave the lab.
So make sure you set the basics right
for observability first
so you can monitor, evaluate,
and then iterate when it matters.