Anthropic just shared a truly staggering software
engineering project. Yeah, the sheer scale completely
blew my mind today. They migrated the entire
bun runtime from Zig to Rust. Mm -hmm. That is
a massive framework translation. It produced
about 750 ,000 lines of code. Functional code,
too. Yeah. And in just 11 days. Right. It achieved
a 99 .8 % test pass rate. Which is astounding
for a code base that huge. But human engineers
didn't actually write this code. No, they didn't.
AI agents did it. And they worked entirely in
strict parallel. Intro music. Welcome to the
Deep Dive. It's great to be here. Today we are
unpacking Claude Code Dynamic workflows. It's
a fascinating look at the future of digital labor.
We really need to look under the hood carefully.
We have some incredibly dense source material
to explore. Here is our roadmap for the discussion
today. First, we're going to explore what these
workflows actually are. Then we'll see how they
differ from familiar Claude features. You might
already be using some of those handy features.
After that, we'll discuss the massive hidden
token cost trap. You definitely need to avoid
that expensive pitfall. Finally, we will show
you how to launch one safely. Step by step, so
you don't blow your entire budget. AI is moving
far beyond simply answering our basic questions.
Yeah, it's now managing massive complex software
development projects automatically. So let's
just jump right into it. Sounds good. We already
know that AI can write code. Right, we've seen
that for a while now. But writing 750 ,000 lines
is totally different. It requires a completely
different processing architecture. We have to
look at how it orchestrates these tasks. Because...
a normal chat session simply cannot handle that
workload. The context window would just break
immediately. Exactly. Dynamic workflows are designed
specifically for these massive tasks. Things
like code base -wide bug hunts. Or comprehensive
security audits across a whole system architecture.
Tasks that are just too large for a normal session.
I like to use a specific analogy for this. Oh
yeah, let's hear it. Think about running a massive
construction site. Okay, a construction site?
Claude acts like your general contractor in this
scenario. Right, the person overseeing the entire
build. Exactly. It doesn't try to build the huge
house alone. That would take way too long. Instead,
Claude looks at your initial project prompt very
carefully. Uh, it reads your requirements. Yeah.
Then it creates a detailed blueprint. The source
material calls this the orchestration script.
Right. Then it breaks the task into much smaller
parts. Like framing a single room or running
the plumbing. Precisely. It basically hires specialized
task workers for the job. So, different AI sub
-agents handle the framing and the plumbing.
And they execute their assigned components in
strict parallel. They don't wait for each other
to finish. Then it brings in the strict city
inspectors. Ah, I see. The verification workers.
Yeah. They check the work, then it hands you
the keys. The keys being the final answer you
receive. Everything runs completely automatically
once you approve it. Let me make sure I have
this process flow correct. Go for it. You give
it a prompt. That prompt generates a workflow
script. Right. The blueprint. Then task workers
execute their specific parts. Simultaneously,
yes. Then verification workers review it. Exactly.
And finally, it produces the final answer. You
nailed it. What triggers the verification workers
to step in? The workflow script defines specific
checkpoints for mandatory review. So reviewers
check the work before moving to the next stage.
Right. And this ensures absolute quality across
the massive code base. You can't have hallucinated
code breaking the entire migration project. It
acts as an automated quality gate. Two secs silence.
Understanding this sheer scale raises a very
important question. Which is? Why do we actually
need a brand new feature today? That's a very
logical question to ask right now. Couldn't we
just use the AI tools we already have? The source
material breaks down these specific differences
carefully. Let's unpack that. What about standard
AI skills? Well, skills provide a reliable, repeatable
process for the AI. They use reusable instructions
for very consistent daily tasks. Right. You might
use a skill for standard code formatting. Or
for generating weekly release notes. But workflows
manage the execution of much larger processes.
So skills are just a repeatable process. Yeah.
For a single worker, workflows manage the whole
crew. OK, what about subagents? Subagents are
really good for a few separate side tasks. Like
asking an agent to check an API key. Right. Workflows
coordinate many related tasks together at once.
They handle a much larger scope of work overall.
Exactly. Wait, I want to push back on something
here. Sure, go ahead. Isn't an agent team basically
the exact same thing? How do you mean? Well,
it's just multiple AI minds working together
on something. It sounds similar, but the focus
is totally different. OK, break that down for
me. Agent teams focus heavily on collaboration
and different viewpoints. So they debate the
best path forward. Yeah. They share complex information
to solve a problem collaboratively. But dynamic
workflows don't do that. No. Workflows focus
purely on strict coordination and rapid execution.
They divide and conquer in strict parallel paths.
Exactly. They aren't debating philosophy. They
just execute the plan. So teams are for collaboration.
Workflows are for coordination. You got it perfectly.
What about the slash goal command feature? The
slash goal command loops continuously until a
condition is met. It focuses strictly on reaching
one very specific outcome. Like fixing a difficult
bug through trial and error. Workflows don't
loop endlessly like that. No, they execute a
specific parallel plan across independent parts.
Can a workflow use a skill? Yes. Individual workers
use them during their assigned tasks. Yes, individual
workers can use skills for specific instructions.
It creates a very powerful combination of different
AI tools. It leverages everything Claude knows
how to do simultaneously. That's why it's so
incredibly effective. Now we've isolated the
core differentiator of this new feature. It's
all about parallel execution at a massive scale.
We should probably define parallel execution
quickly. Good idea. It means running multiple
independent tasks at the exact same time. Right.
It removes the bottleneck of sequential processing
entirely. Traditional AI works step by step in
a straight line. But workflows expand a task
into multiple simultaneous paths. Let's look
at what that actually looks like in practice.
When is it actually worth your time to use this?
Yeah, when should I spin up a dynamic workflow?
They provide the most value for very large, divisible
tasks. The project must be easily broken into
standalone parts. Exactly. Large code -based
reviews are a perfect use case here. You might
need to review hundreds of files across modules.
Claude can analyze multiple modules at the exact
same time. Then it combines all those findings
into a single report. It's incredibly efficient.
Skill audits are another really great example
from the source. Yeah, that's a very practical
application for developers. You might need to
evaluate many skills using identical criteria.
Each skill gets reviewed completely independently
by different workers. Then the system combines
the results perfectly. It keeps your AI tools
sharp and optimized. Multi -part research tasks
fit this parallel model perfectly well, too.
You can have agents scouring different databases
simultaneously. One checks API docs? Well, another
checks legacy code bases. Then they combine their
findings into a cohesive report. Beat. Let's
bring back that bun migration example from earlier.
Right. Sick to rust. This involved huge framework
and language syntax changes. It required hundreds
of agents running entirely in strict parallel.
Whoa. Imagine scaling to hundreds of agents working
simultaneously. It completely changes how we
think about manual tech labor. Framework migrations
usually require massive amounts of human engineering
effort. API upgrades normally take weeks of tedious
manual review. Imagine manually rewriting every
single API call in a project. Humans get tired,
they make typos, they lose focus easily. But
the AI agents maintain perfect consistency across
hundreds of files. And they tackle the boring
parts of software engineering in Why are file
migrations a perfect fit for this? Because Claude
can analyze different directories without getting
confused. Because Claude can analyze multiple
affected areas all at the same time. It saves
countless hours of painful, repetitive manual
code checking. It frees human engineers to focus
on high -level system design. Mid -roll sponsor,
Read. Seeing hundreds of agents spin up is undeniably
thrilling today. It really is. But this brings
us to the dark side of this feature. We definitely
need to discuss the hidden token cost here. It
is a very serious consideration for anyone using
this. This is the biggest risk with dynamic workflows
by far. They can consume substantially more tokens
than a normal coding session. Anthropic specifically
warns users about this massive potential burn
rate. It can get incredibly expensive if you
aren't paying attention. We should quickly define
token usage for clarity here. It's the cost units
AI uses to read and write text. Exactly. It's
how you get billed. Why does token usage explode
so rapidly with these workflows? Well, a normal
chat session uses one main context window. You
just chat back and forth on a single thread.
Right. But workflows are very different structurally
behind the scenes. I'm sure. Multiple workers
need access to the exact same initial instructions.
Oh, I see. They all need the same files and overarching
project context. So the same context gets sent
over and over again. Yes. Dozens of workers mean
massive token burn across the board. Even if
the final output report is completely short.
Yes. Because each worker performs its own independent,
isolated analysis. Each one needs full system
context to do its job. If 50 agents need a 10
,000 token file, That multiplies fast. It's like
paying 50 people to read the same massive book.
And every single worker reads chapter one before
starting work. Wow. If your initial context window
is 50 ,000 tokens long and you spin up 20 different
parallel task workers simultaneously. You just
consume one million tokens just setting up the
board. Exactly. The math gets incredibly scary
very quickly. So how do we avoid this massive
token waste? The solution is defining a very
narrow specific scope. I still wrestle with prompt
drift myself, honestly. It's a common struggle.
Defining a hyper exact scope is surprisingly
difficult to do well. It's difficult but it's
absolutely essential for dynamic workflows today.
You must define the exact objective clearly before
starting anything. You must Isolate the exact
files or folders involved initially. You must
declare the expected output format very precisely,
too. You can't just ask Claude to, you know,
fix your app. A smaller scope helps Claude create
a much more focused workflow. It prevents the
AI from exploring completely irrelevant code
paths. Which saves your valuable tokens in the
end. A well -defined workflow uses far fewer
tokens than broad requests. Always ask if you
really need massive parallel execution first.
Sometimes a simpler feature solves the problem
much cheaper anyway. Does Claude warn you before
spending all those tokens? Thankfully, yes. It
shows a clear confirmation screen first. Yes,
Claude always shows a confirmation screen before
the workflow actually starts. It estimates the
complexity and asks for your explicit permission.
That's a very thoughtful design choice by the
Anthropic team. It ensures you never spend tokens
completely by accident. We know the massive token
burn risks involved with this now. Right? How
do we practically put our hands on the keyboard?
You want to know how to trigger one safely. Yeah.
How do we do this without blowing our budget?
First, you don't write the workflow scripts yourself
manually. Claude builds the entire workflow automatically
for you. It bases everything on your clear initial
project description prompt. You just tell it
exactly what you want to achieve. Let's walk
through a concrete example from the source material.
Okay, what's the scenario? We want to audit a
library of local Claude skills. Those are usually
stored in the .Claude slash skills directory.
Exactly. Step one is defining the strict project
scope first. I can tell Claude to identify duplicated
instructions across files. Or you might ask it
to find outdated guidance there. And I must ask
for a report with specific actionable recommendations.
Right. Step two is letting Claude build the workflow
automatically. So Claude handles all that complex
orchestration behind the scenes. You don't design
the execution process manually at all here. It
breaks the task apart and assigns the virtual
workers. It writes the orchestration script based
on your initial prompt. Step 3 is reviewing the
execution plan carefully before proceeding. You
really need to check this before you click approve.
Right, and this prevents a surprising amount
of wasted token usage. You can see exactly what
files the agents plan to read. Make sure it isn't
analyzing more than strictly necessary. If it
wants to read your node module's folder, stop
it immediately. Step four is reviewing the final
comprehensive report it generates. This is honestly
the true beauty of the entire system. It shows
affected files, supporting evidence, and specific
recommended fixes. The workflow synthesizes all
those parallel findings into one document. Without
this, you'd be opening and reviewing every single
skill file manually. It highlights exactly what
needs to change in your code base. Step five
is saving good workflows for future repetitive
use. Right. You don't want to recreate the wheel
every time. Can I reuse a workflow once it's
built? Absolutely. You just save it for the next
time. Absolutely. Just save it to your local
Claude workflows folder for next time. You don't
have to generate the orchestration script from
scratch. So you basically build a personal library
of reusable processes. It standardizes how you
audit your code across the board. Two sec silence.
Let's zoom out from these tactical steps for
a minute. Good idea. We need to understand the
underlying philosophy of the source material.
The golden rule is choosing the right feature
for the job. You don't always need the most advanced
tool available today. That is the core message
we want to convey today. Use standard Claude
code for quick, simple, daily coding tasks, use
standard skills when you have repeated consistent
automated tasks, use subagents when you have
minor supportive side tasks, use agent teams
for collaborative multi -viewpoint problem -solving
sessions, use the slash goal command for iterative
condition -based loop tasks, and save dynamic
workflows for large truly independent parallel
tasks. Dynamic workflows only make sense with
massive parallel execution architectures. If
tasks must happen sequentially, do not use a
dynamic workflow. If step B requires step A to
finish, avoid workflows entirely. Match the AI
tool directly to your specific problem shape.
It saves time, saves money, and produces much
better results. It's all about working smarter,
not harder. If AI can orchestrate hundreds of
parallel agents successfully today, if it can
port entire massive code bases in 11 days. With
99 .8 % accuracy, no less. What does the future
of a one -person tech startup look like? It looks
completely different than it did just last year.
Perhaps your primary job is no longer writing
code manually. You transition from a manual coder
to a general contractor. Perhaps your job is
simply defining tight scopes for your AI workforce.
You manage the grand vision while the AI swings
the hammers. I want you to audit your repetitive
tasks this week. Look closely at your daily work.
Ask yourself, could this task be broken into
parallel, independent parts? Think about how
you construct your daily blueprints going forward.
Thank you for joining this deep dive together
with us today. It's been a great conversation.
Take care and keep exploring the possibilities
safely out there.