I spent three hours last week debugging what should have been a simple integration issue. A Flow was calling an Apex method that was making an HTTP callout to a MuleSoft API, and somewhere in that chain, data was getting mangled.
The problem? I had to use four different tools to trace through the execution. Flow debug logs in Setup. Apex debug logs in VS Code. MuleSoft logs in Anypoint Studio. And then Runtime Manager for the actual API execution logs. Each with different timestamp formats, different error codes, and no way to connect the dots.
It’s 2025. We shouldn’t be debugging integrations like it’s still 2015.
The Reality of Modern Salesforce Development
Here’s the thing that’s changed: Salesforce development isn’t just Salesforce anymore. When Salesforce acquired MuleSoft, we didn’t just buy an integration platform, we bought into a much more complex development ecosystem.
Now we’re building solutions that span declarative flows, custom Apex logic, and full-blown API orchestration. And every piece has its own tools, its own deployment model, its own way of handling errors.
Customers are building these amazing end-to-end solutions, but the development experience is fragmented. You start in Flow Builder, jump to VS Code for your Apex, then over to Anypoint Studio for your integration logic. Each context switch costs you mental cycles.
What Debugging Actually Looks Like
Let me give you a concrete example from a demo that I was working on recently:
- Started with a Flow when someone filled out a web form
- Called an Apex method to validate the customer data
- Made an HTTP callout to a MuleSoft API
- That API called DocuSign for document signing
- Then updated the Salesforce record when everything was complete
When something went wrong (and something always goes wrong because I do dumb stuff sometimes), here’s what debugging looked like:
First, I’d check the Flow debug logs to see if the Apex method was even getting called. Then I’d look at the Apex debug logs to see if the HTTP callout was working. Then I’d check the MuleSoft application logs to see if the request was coming through. Then I’d look at the DocuSign webhook logs to see if that part was failing.
Each system had a different correlation ID. Different timestamp formats. Different ways of showing request/response data. It was like being a detective with evidence scattered across five different crime scenes.
The Tools Don’t Talk to Each Other
This is the fundamental problem: all these tools were built in isolation. Flow Builder doesn’t know about MuleSoft. Anypoint Studio doesn’t understand Salesforce metadata. VS Code treats your Apex code like any other Java-ish language without understanding that it’s part of a larger platform.
When building internal developer tools, we talked about this stuff all the time. How do you give developers a cohesive experience when the platform is getting more distributed? How do you maintain that “everything just works” feeling when you’re orchestrating across multiple runtimes?
The answer isn’t to build one tool that does everything. It’s to make the tools aware of each other.
What Better Would Look Like
Imagine this: you’re looking at a Flow element that calls an Apex method. You right-click and select “View Implementation.” VS Code opens to that exact Apex method. You see an HTTP callout in the code and right-click again. Anypoint Studio opens to the exact API endpoint that’s being called.
Or better yet: you’re debugging a failed transaction, and you get a single trace that shows you the Flow execution, the Apex method calls, the HTTP request/response, and the MuleSoft processing—all in chronological order with the same correlation ID.
The technology exists. We just need to connect it.
The AI Opportunity
Here’s where it gets interesting from an AI perspective. LLMs are really good at understanding context and relationships. But they can only work with what they can see.
When your Apex code, Flow definitions, and MuleSoft applications are all isolated, an AI assistant can’t help you understand how they work together. It can explain your Apex method or suggest improvements to your DataWeave transformation, but it can’t tell you that your Flow is passing the wrong data structure to your API.
A unified development experience would give AI tools the context they need to be actually helpful across the entire solution stack.
The Path Forward
What we need is a shared foundation. Common metadata formats. Shared correlation IDs. APIs that let tools communicate with each other. A deployment model that understands cross-platform dependencies.
None of this requires rewriting everything from scratch. It’s about adding integration points and shared standards to tools that already work well individually.
Why This Matters
At the end of the day, developer experience isn’t just about making tools easier to use. It’s about reducing the cognitive overhead so developers can focus on solving business problems instead of fighting with tooling.
When you’re spending 30% of your debugging time just figuring out which tool to look at next, that’s not a tooling problem, that’s a developer productivity problem.
