When I mention using AI to accelerate the Finix integration, people might want to know what that actually looked like day-to-day. The reality was both more mundane and more powerful than expected.
I'd been experimenting with Claude Code for a few months before this project, but payment processor integration felt like the perfect test case for something more ambitious. The core challenge was that I needed to understand a complex domain quickly while building, so I could design better.
In addition to reading Finix docs, I had Claude Code parse their entire API reference and generate working examples for relevant endpoints, at least the ones I knew the purpose of at the beginning.
Every time I learned something surprising about Finix—like how payment methods needed pre-authorization even for small amounts, or how the sandbox environment behaved differently from production for ACH processing—I'd update the CLAUDE.md file. This simple markdown based file became a way of building knowledge and memory.
I took this a step further by creating an .md file in every relevant folder in my project—at least one sublevel down. Each folder got its own focused documentation: the payments folder tracked authorization quirks, the webhooks folder documented retry behavior, and the testing folder captured sandbox vs production differences. This decentralized approach created a kind of progressive disclosure—Claude Code only loaded the context relevant to the files it was working with, rather than ingesting everything at once. Information stayed close to the code it described, surfacing exactly when needed.
The file also tracked dead ends and failed approaches. When Claude Code suggested something that looked good in theory but failed in practice, I'd document that too. This prevented me from going in circles and helped build a more realistic understanding of what actually worked.
I treated each development session like a focused work sprint—loading the specific context needed, solving a particular problem, then documenting the learnings, and then committing to GitHub. Each session was typically self-contained enough to pick up where I left off, which probably led to better architecture.
I leaned heavily on Claude Code's built-in MCPs—specifically git, command line, NoSQL, and Github integrations. This gave Claude Code direct access to my codebase, the ability to run tests and API calls, and connection to my GitHub to manage the project. My commits were so in-depth. If I needed to reference anything, I had a pretty strong record of my history. Claude could read this history, too.
Halfway through my project, Anthropic introduced a new Subagent feature, which I quickly integrated into my workflow. Each subagent runs in its own separate context window and does not share memory or conversation history with other subagents or the main agent. They operate in isolation from each other and help reduce the risk of context spillover during instruction following.
I think getting a fresh context window, with each subagent call, is probably the most useful element, though. The less cluttered the context window, the stronger Claude seemed.
Payment Patterns Agent
Loaded with general payment processor patterns and best practices
Security Agent
Focused on PCI requirements and compliance validation
Infrastructure Agent
Entirely geared toward infrastructure design decisions
Debugging Agent
Specialized troubleshooting that came in very handy
I had to start the entire project over, totally from scratch, at least twice, because I learned something so significant that refactoring the code would have been too messy.
If I was coding by hand, I probably would not have started over, but, honestly, I might not have been in such a perilous position in the first place.
Overall, this approach isn't magic, and it's probably not appropriate for every project. Payment processor integration seemed to work well because it's a domain with clear technical constraints and success criteria.
The AI could be wrong about user interface preferences, but it couldn't be wrong about Finix's API requirements—those either work or they don't.
AI helped me understand the domain deeply enough to make good architectural decisions on the fly, rather than just hacking things together and hoping they'd work out later.
The real win was that by the time I started really designing the user interface, I understood the system deeply enough to make design decisions that matched technical reality instead of fighting against it.