Narrator: So let's review the characteristics
of Deep Agents.
And I want to show you a concrete example how they manifest.
And while Deep Agents is a general term,
I think the ones who coined this term
were the LangChain team.
And they did an amazing job articulating this
and coming up with this architecture
after analyzing a bunch of Deep Agents implementations.
All right, so let's go back to the architecture
and let's go and talk about what makes a Deep Agent.
And let's start with the planning tool.
So every Deep Agent you are going to see,
it's going to have a planning tool.
And here we can see as an example, Claude Code.
And here we have a bunch of tasks that are already done.
We have one task that is in the making
and is currently executing,
and after it we have other tasks which needs to be executed.
So what we're seeing here is not implicit planning
through chain of thought reasoning
we know from large language models.
Deep Agents use explicit planning tools.
And this is typically being implemented
as a to-do list in a markdown format.
And between execution, the agent is going to actively review
and update this plan.
So this plan is highly dynamic.
The agent is going to mark tasks
as pending, in progress or completed.
And if we're going to have a task that failed,
the agent is not going to blindly retry to do it
like we know from the original React algorithm.
So the planning tool is going to help us steer the agent.
It's going to be continuously updated.
And we as the users, we can also influence
the task list and the to-do list.
And in Claude Code specifically,
the planning tool is internal,
so we don't have direct access to it,
however we can see it executing.
And what you're seeing right now
is a X post by Boris Cherny, which is the creator
of Claude Code by the way.
And here you can see the update to do,
which is calling the planning tool to update the to-do list.
And this to-do list is going to be continuously updated
by the Deep Agent.
And this is going to yield us better results
and to actually accomplish those complex tasks.
And when thinking about it, this is pretty intuitive
because we as humans, if we have a complex task,
we usually want to break it down.
We want to keep track of what we have accomplished so far.
So this also give us a dopamine rush,
you know, to check a task that is completed.
And it really shows the progress of our project
and something complex that we are trying to achieve.
So the idea here is actually very intuitive.