Imagine upgrading from a bicycle to a fully autonomous self
driving car overnight. I mean, that sounds like a
pretty great upgrade to me. Right.
It sounds amazing, but then imagine you're suddenly flying
down the highway at 80 miles an hour and you realize, wait,
nobody's actually paved the roads yet.
Yeah, that is a massive problem. Yeah, and that is the exact
crisis hitting the software industry today.
Welcome to the Deep Dive. Today we have a massive stack of
sources from across the development world.
We've got developer blogs, industry analysis, Reddit
threads, and honestly a massive internal code base leak from
April 2026. Which is just a wild story on
its own. Completely wild and all of these
sources are pointing to the exact same massive shift.
AI has officially graduated. Like we are no longer talking
about a fancy auto complete that just finishes your sentences in
the editor. Right.
We've moved way past that. Exactly.
We are dealing with autonomous agents that just go off and
execute entire workflows entirely on their own.
So our mission for you today, whether you are a seasoned
developer, managing A-Team, or just insanely curious about
where AI is heading, we're going to explore what actually happens
when we hand over the keys. Because the transition from, you
know, passive assistance to active agency, it basically
breaks almost every assumption we have about how software is
built. Let's talk about how we even
know this is breaking, because we got a very unexpected look
under the hood recently, and it's all thanks to a pretty
embarrassing operational security failure over at
Anthromic. Yeah.
The clawed code leak. Yeah, the clawed code leak.
They literally accidentally left their own clawed code source
repository public on GitHub. Which, you know, happens when
you're moving at that kind of breakneck speed, but still.
Right, but the funny part is how they handled it.
The issue to DMCA takedown request to scrub it from the
Internet, which honestly just functioned exactly like firing a
flare into the night sky. Oh, total Streisand effect A.
100% the moment they tried to hide it, outlets like the new
stack and latent space just descended on it with these
incredibly deep architecture analysis.
And what those analysis uncovered is just crucial for
anyone using these tools because it really strips away this
illusion of a a hyper intelligent, omniscient AI
brain. Right.
It pulls back the curtain. So what did the leak actually
reveal about what's going on in there?
Well, it exposed the raw mechanics of their agentic loop
scaffolding, so it showed precisely how the prompt
structures the tool you sequencing.
And these relentless error handling loops are layered
around the core model. So it's not just a smart model
doing everything natively. No, not at all.
It's a deliberate, highly engineered system to create the
illusion of autonomy. Like in a standard that
interface, the model just generates a response based on
its internal weights and then it stops.
You asked a question, you got an answer.
Right, but the Claude code architecture relies on this
persistent evaluation loop. The system prompt actually
injects a hidden scratch pad for the model to use for its
intermediate reasoning. OK, so it's like talking to
itself before it talks to you. Exactly.
And it wraps these thoughts in specific XML tags.
Like literally a thought tag and an action tag.
So say the model decides it needs to run a bash script to
check a file. The scaffolding catches that
action tag, executes the script in a hidden terminal, grabs the
output or the error message, and then feeds it directly back into
the models context window. And then tells it to evaluate
what just happened. Yes.
And if the script fails, the scaffolding literally forces the
model into a retry loop based on the specific error code it got.
But wait, let me push back on this a little bit.
If the secret sauce driving this whole new era of AI is
essentially just a highly organized, automated To Do List
looping over itself, I mean, doesn't that kind of kill the
magic? In what way?
Well, where is the actual intelligence?
Aren't we just talking about standard boring automation
bolted onto a language model? How does knowing about these
scenes actually change how you type a prompt into the tool
today? I get that, but the intelligence
is in the model's ability to semantically interpret the
failure state and rewrite its own approach on the fly.
The automation is just the chassis of the car, right?
But understanding this mechanism fundamentally changes how you
work with it. The leak basically proves that
you need to stop spending time endlessly tweaking your prompt
to have the exact perfect wording, and instead start
spending your time building rigid fault tolerant scaffolding
around the model. Oh, because it's a looping
machine. It's going to generate output at
an unprecedented speed, and it's going to make mistakes.
Exactly, which is why developers like Simon Willison are
aggressively exploring sandboxed iframes for things like Claude
artifacts. Right, I saw that in the
sources. Because if you know the agent is
going to relentlessly loop and execute code, you definitely
can't just let it run wild on your local machine.
No, it'll destroy your environment.
So developers are building these isolated sandboxed iframes.
That way the agent can render its own UI, run its own
JavaScript, test its theories, all without nuking the host
machine. You don't trust the agent to be
correct right away. Right, you just trust the
sandbox to contain the mess while it iterates.
And because it never gets tired, it just iterates so fast.
Which actually brings us directly to the massive traffic
jam happening at the human level.
The review model neck. Yes, The New Stack published
this really fascinating piece about the hidden PR tax and how
agentic AI is just actively breaking CICD pipelines right
now. Well, yeah, because our software
pipelines were designed around the biological limits of human
typing speed and, you know, human cognitive load.
Right, we can only type so fast. Exactly.
So when you flip the paradigm and suddenly an agent can
generate a massive pull request touching 20 different files in 3
minutes, the bottleneck instantly shifts.
Writing code is no longer the rate limiting step.
Reviewing the code is. Exactly, reviewing is the new
bottleneck. And it seems like management
teams are just totally stuck in what the articles call phase
one. They buy everyone Claude Code
licenses, they roll it out, and they just celebrate these
amazing velocity metrics. Yeah, line output goes through
the roof. But they have not braced for
Phase 2 at all, and Phase 2 is when that downstream avalanche
of code hits the senior engineers who actually have to
review and approve it. And that creates a severe crisis
because senior engineer time is basically the scarcest resource
in any tech organization, and the nature of the AI code makes
it uniquely exhausting to review.
Let's talk about why it's so exhausting, because there's a
detail here that is just fascinating to me.
AI generated code looks incredibly confident.
Yes, dangerously confident. Like if you have a human junior
developer submitting a really complex database migration,
they'll usually flag their uncertainty right?
They'll leave comments As for architectural guides and say
like hey, not sure if this is going to scale.
Right, there's humility there, but agent generated code carries
0 humility. The AI will output a completely
hallucinated API endpoint or just a subtly flawed encryption
wrapper, but it formats it with the exact same authoritative
styling and variable naming as a perfectly optimized algorithm.
It's basically an army of highly caffeinated interns who never
sleep and absolutely never admit when they're guessing.
That is exactly what it is, and so the cognitive load required
to review that kind of heavily refactored, highly confident
code is immense. The senior dev has to trace
every single logical leap the agent made, but without any of
the human context. And the scary part is, according
to a post on the Stack Overflow blog, we can't even fall back on
our automated test suites to catch these errors.
Right, because traditional testing assumptions are
completely breaking down. How so?
Like why doesn't a unit test just catch the bad AI code?
Because our entire testing philosophy historically has been
built on determinism, you assert that a specific input will yield
a highly specific, immutable output every single time.
OK, simple enough. But if your application is now
relying on an agent to dynamically generate data
transformation logic on the fly, the output shape might subtly
shift between runs. It's non deterministic, so your
strict assertion tests are going to start throwing red flags
everywhere. Oh wow, so the code isn't
necessarily broken, but the test fails anyway.
Exactly. The testing paradigm itself is
becoming obsolete in an agentic world.
How are organizations actually handling this?
Because the sources talk about a few mitigations.
You have companies like Gradient Labs, right?
They're deploying GPT 4.1 to power AI account managers in the
banking industry. Which is highly regulated.
Super regulated in banking. A hallucination isn't just a
bug, it's a massive compliance violation.
You can't just rely on an exhausted human reviewer to
catch every confident error an AI makes.
Right, So what gradient labs and others are doing is relying on
aggressive pre review automation.
Like automated toll booths before the human even sees it.
Exactly. Before a human ever lays eyes on
the PR, you force the agents code through a gauntlet.
You use semantic linting, static analysis and property based
testing. You validate the characteristics
of the output rather than looking for an exact string
match. The sources also uniformly agree
that you have to keep AI generated pull requests
extremely small and explicitly tag them as AI generated.
Right, because if you let an agent rewrite an entire back end
service and submit it as one giant chunk, your senior
engineering team is paralyzed for a week.
But if you restrict the size of what the agent can do, doesn't
that defeat the purpose? Like, doesn't that limit the
utility of having an autonomous agent in the first place?
It does, which is why the industry's next move is
horizontal scaling. If one agent submitting a
massive block of code breaks the pipeline, the solution is to
deploy multiple agents working on micro tasks simultaneously.
OK, and that brings us right to this new feature.
The GitHub blog posted about the slash fleet command in the
Copilot CLI. This feels like a massive
paradigm shift. Oh, it really is.
Because you're no longer just talking to a single assistant,
you are basically dispatching an entire swarm of parallel workers
right for your command line. Yeah, and the mechanics of slash
fleet demand an entirely new skill set from developers.
You're orchestrating multiple agents across a code base.
One agent might be updating the front end React components,
while a second agent is simultaneously writing the
database migration and a third is updating the API docs.
But there is a huge catch here. The GitHub blog explicitly
details a major failure mode with this swarm approach.
If you don't perfectly declare the interagent dependencies,
they just trip over each other. They absolutely destroy each
other's work if the database agent decides hey I'm going to
rename this primary key to optimize a query, but the front
end agent is currently binding data to the old key name.
Then the whole code base is just fractured completely.
Fractured. It's not magic, so developers
have to learn task decomposition.
You have to foresee the friction points before you launch the
fleet. You have to explicitly tell the
front end agent hey, you must wait for the database agent to
finish its schema before you even start.
But wait, let me jump in here. Because if I have to spend hours
perfectly mapping out this intricate dependency graph,
telling 5 different agents exactly how to interact, aren't
we just trading the chore of writing code for the equally
tedious chore of writing impossibly detailed
instructions? That's fairpoint.
Like are we actually saving any time here?
You are, but you're shifting the burden.
You're moving away from writing syntax and moving into the
architectural layer, but we're already seeing solutions to that
specific orchestration burden. Really like what?
The Copilot applied Science team actually just published findings
on Agent Driven Development. They are actively using agents
to build and orchestrate other agents.
Wait, so a manager agent gets the humans broad objective,
breaks it down, writes the dependencies and then launches
the worker? Agents.
Precisely. The manager agent analyzes the
code base, realizes the database migration, naturally blocks the
front end, and it just automatically constructs the
slash fleet execution plan for you.
Managing a swarm of agents like that sounds exhausting,
especially because of a really weird cork with these systems
that the sources highlighted. Every single morning your entire
swarm wakes up with complete amnesia.
Yes, persistent episodic memory is basically the Holy Grail
right now. Because according to the news
stack, there's this massive race between projects like Open Claw
and Hermes Agent to solve this. Right now every AI coding
session starts from Absolute 0. Right, you know developers are
using things like the clod OD dot MD file to help with this.
You put a markdown file in your root directory that just says
hey we use TypeScript, we prefer functional components.
Never use this one deprecated library.
OK, so rules of the road. Exactly.
But that's just static project state, it doesn't give the agent
episodic memory. Right, so if I spent 3 hours
yesterday arguing with the agent about some weird race condition
in the payment gateway and I log back in today and say OK, let's
finish fixing that bug. The agent has absolutely no idea
what you're talking about. It's so frustrating.
It's terrible UX. To regain the context, the agent
basically has to reinjest the entire history of the project,
all the static rules and the current state of the code base.
It's just a brute force solution and it completely interrupts
your flow state. And it's not just a UX problem,
right? That brute force memory
retrieval has a massive financial cost.
If you go on to the developer subreddits right now, the front
pages are just filled with users freaking out because they are
hitting their API limit and burning through their token
budgets in literally hours. Oh yeah, the token economics of
an agentic loop are brutal, and users are completely
miscalculating it because they're bringing chatbot
expectations into an agentic reality.
Let's break that down for the listener.
Why is the token burn so much worse with an agent?
Well. Think about a chatbot.
You ask a question, you consume maybe a few 100 tokens for the
prompt and a few 100 for the answer. 1 discrete transaction.
Simple. But when you deploy an agentic
loop to fix a bug, the model looks at the code base, writes a
script, executes it, and evaluates the error.
That's just loop 1. OK, if it fails, it has to try
again. But to maintain its train of
thought for loop 2, it has to pass your initial prompt, the
script it just wrote, the error it got, and its new hypothesis
all back into the context window.
Oh I see. So the context window just
compounds on itself every single time it loops.
Exactly. By loop 10 the agent is
ingesting 10s of thousands of tokens just to remember what it
already tried 5 minutes ago. We're literally paying for the
agent to reread its own massive internal monologue over and
over. So the token consumption rate
and ends up being 10 to 20 times higher than a standard chat
interaction. It's.
Literally the equivalent of hiring a general contractor to
come to your house just to screw in a light bulb.
Like yes they can do it, but it's massive overkill and you're
going to drain your budget. That is the perfect way to look
at it. You have to map the tool to the
complexity of the problem. Single shot prompts are for
localized syntax generation. Save the agentic loops for
complex multi file refactoring where the time you save actually
that exponential Turk and cost. This entire deep dive really
highlights how much software engineering is recalibrating
right now, and when we started with that clawed code leak which
showed us that AI autonomy is really just relentless looping
scaffolding under the hood. Not spontaneous magic.
Right, and we saw how the sheer speed of that loop shifts the
bottleneck away from writing code and just Burry senior
engineers under an avalanche of confident, non deterministic
PRS. Which is forcing teams to adapt.
They're deploying pre review automation, they're using GitHub
slash fleet to run multiple micro agents at once, and
developers are having to master task decomposition.
And of course, navigating the whole amnesia problem, plus the
massive token taxes required just to force these models to
remember what they did yesterday.
It really feels like the developer's job is shifting from
writing syntax to orchestrating fleets, managing token budgets,
and basically building fault tolerant sandboxes.
It is, and that trajectory actually brings up a deeply
provocative question about where this is all heading.
We mentioned Simon Willison earlier in his work with
sandboxes. In a recent discussion, he
brought up this concept of dark factories in the manufacturing
world. Have you heard of this?
I think so. Is that where the physical plan
is so fully automated that they literally don't even turn the
lights? On exactly there are no human
operators on the factory floor, so the entire assembly line just
ones in the dark. Wow, OK, that's a wild image.
Now apply that concept to what we've been talking about today.
We already have the Copilot applied science team using
agents to orchestrate other agents.
We know the ultimate bottleneck right now is the human senior
engineer who has to review the code.
Right, humans are the slow part of the pipeline.
So how long does the human actually remain in the loop?
If an agent can write the code, and a manager agent can
orchestrate the dependencies, and a third agent can run the
pre review semantic analysis to ensure it's safe?
How long until we just decide the human reviewer is too slow
and we replace them entirely? Oh wow, we have to ask
ourselves, are we rapidly approaching the era of software
dark factories where code is designed, written, reviewed and
deployed entirely by machines, and we're left with a digital
infrastructure that no human actually comprehends anymore?
That is a static, staggering thought.
The self driving car isn't just navigating the unpaved roads we
talked about at the start, it's basically paving its own routes,
communicating in protocols we barely understand and operating
with the windows completely blacked out.
Moving at speeds we can't even review.
It fundamentally challenges what it means to be a software
engineer in the coming decade. Definitely something for you to
consider the next time you casually authorize an agent to
rewrite your repository. Thanks for joining us on this
deep dive.