Agentic coding, powered by AI, is revolutionizing software modernization by enabling autonomous code understanding and task execution, significantly reducing the time developers spend deciphering legacy systems and accelerating the delivery of new features.
Mind Map
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Here's something most software teams know about but don't always talk about. How
developers oftent times spend more time trying to understand existing systems than they do
writing new code. In fact, studies show that about 60 to 70% of developer time goes toward
understanding context before a single change can be safely made. That's a lot of wasted time.
It's like being handed the keys to a house that you didn't build. Sure, you can see the rooms,
but you don't know why there's a light switch in the closet or why one bathroom pipe connects
to the kitchen. You need to figure all of that out before you can safely renovate.
Today we're talking about how agentic coding is changing that and how software development
teams are using it to tackle one of their biggest challenges, modernization. Now,
let's take a step back for a second. Agentic coding means AI systems that can autonomously navigate
and understand code bases, propose changes, and execute tasks with minimal human intervention.
and modernization. That means taking an existing application and evolving it to meet new system
requirements and use cases without breaking what's already working. We'll be following an example
today of how dev teams are using agentic coding, why legacy code like Java can be tricky to change,
and then how this impacts the dev process overall. Let me set the scene to make this real.
We're going to put ourselves in the shoes of a financial services company that's running
a large Java application. This thing handles customer accounts, loan processing, payments,
and reporting pipelines all in one place. A requirement is introduced to add real time AI
powered risk scoring so customers can get instant loan decisions. Sounds great, right? But to get
there, the team needs to modernize. As mentioned earlier without breaking anything that's already
working. And that's where it gets interesting for our team. The challenge with modernization
isn't actually the old legacy code itself. It's lost understanding. Think of it like an iceberg.
The code that you can see is just the tip. Below the surface is everything that's disappeared over
time. Why the loan approval process run checks in a specific order. Why a scheduled reporting
job runs at 2 am. Why define timing constraints or why changing one line in a payment flow once
froze customer accounts for 4 hours. Our team may have not written this down. The people that built
it could have moved on and that knowledge is now buried under years of patches and updates.
So let's talk about three things that make this especially difficult for our team as they try to
add in the new risk scoring tool. First is tangle dependencies. Different parts of the application
have become deeply connected over time in ways the team never fully mapped. Change the way an
interest rate is calculated and suddenly the compliance reporting system starts flagging
the wrong accounts. Why? Because two were quietly sharing a database table that no one on the team
knew about. Second is framework gaps. The system runs on Java 8, which handles threading and memory
management very differently from modern versions. Something as routine as upgrading to Java 17, can
surface dozens of hidden compatibility issues and libraries that the team hasn't touched in years.
Third is undocumented external connections. This system connects to external credit bureaus,
payment networks, and regulatory reporting systems that each have their own strict data
format requirements. Change how a single field is formatted internally, and a nightly compliance
report could start rejecting submissions without anyone on the team immediately understanding why.
This all of this right here can contribute to that 60 to 70% of developer time disappearing and it's
why our team hasn't been able to move forward on the real-time risk scoring that the business
is asking for. Every time they get close, the complexity of what might break pulls them back.
And these are exactly the kinds of problems an AI development partner is designed to solve.
Rather than leaving our devs to untangle all of that complexity manually, an AI development
partner steps in to do the investigative work alongside of them. Think of it as acting like
a building inspector who can x-ray the entire system, trace every wire, and map every pipe
before anyone picks up a hammer. And importantly, this doesn't happen as a one-time pass, but rather
as part of an iterative modernization cycle. We need to understand what exists, make a safe
change, validate it, and then expand from there. Modernization will look a little different for
every team, but here's what the cycle is like for our financial services team as they work to pull
the loan decisioning logic into its own system. Step one is to analyze the codebase. Our AI
development partner analyzes the application and builds a working model of how everything connects.
Call paths, data flows, and hidden dependencies. And almost immediately, it surfaces something
that the team didn't know. Every loan application actually triggers a very specific sequence; credit
checks, watch list reviews, compliance validations in a strict order that was never written down
anywhere. Without that discovery, the team would have separated the wrong logic. Loan decisions
would start coming back incorrect, and that's the kind of issue that shows up in a compliance report
very quickly. That risk just got removed before any code was changed. Step two is identify safe
boundaries for change. With that model in place, our AI development partner proposes where it's
safe to separate the loan decisioning logic and not just what to extract, but what depends on it,
what it depends on, and how the new service should communicate with the rest of the system.
It flags edge condition, shared data, hidden coupling. So the team isn't guessing where
to draw the line. They're instead validating a boundary that's already been mapped. Step three
is to generate test coverage. Before anything is changed, the AI development partner generates
a broad set of tests based on what it found. Not just happy paths, but edge cases, including credit
check timeouts, partial application failures, and customers getting flagged mid-process. It builds
coverage around real behavior, not just what the team assumed the system was doing. Is it possible
in every scenario? No. But it's far more coverage than the team would realistically have to create
from scratch, especially under time pressure. Step four is that our devs review and approve. This
is where the developers step back in. They review the proposed boundaries, validate assumptions, and
check the generated tests and then approve each step before anything moves forward. What they're
not doing is spending weeks digging through legacy code trying to figure out what talks to
what. They're coming in with their judgment. Step five is to run old and new side by side. Once the
new loan decisioning service is introduced, it doesn't immediately replace the old one.
It's actually going to run next to it. The same input, two outputs. If there's any difference,
the AI development partner flags it instantly. full context, what changed and where it diverged,
so the team can fix issues early before a customer sees it and before it hits a regulatory report.
Now, letting AI work on a system that processes real loan applications, one that feeds compliance
reports, and one that regulators expect to be right every time, that's not something a
team should ever take lightly. This only works if control, validation, and traceability are
built in from the start. For any team, this shows up in three very specific ways. First
being human approval. Any changes, especially those affecting reporting pipelines, have to be
reviewed and approved before it goes anywhere and by a developer. Secondly is full change history.
Everything's in Git, easy to review, easy to roll back. And lastly is no autonomous deployment.
Devs are always in the loop before anything goes live. These safeguards make the whole
approach trustworthy enough for a team to use on a system that real customers and regulators depend
on every single day. Instead of a bunch of time navigating old code, our dev team delivered that
real-time risk scoring feature on schedule. The time previously lost to legacy code archaeology
went straight to the development work needed. So, how is your team handling modernization right now?
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