AI is changing how we build software, but I think a lot of developer tools are solving yesterday's problem.
Many products proudly advertise that they're "AI-powered," but when you actually use them, the experience usually looks like this:
- Ask AI to generate something.
- Copy the output.
- Paste it into another tool.
- Run it.
- Copy the results back.
- Ask AI what to do next.
The AI did the thinking.
You did the integration.
The hidden bottleneck
This became obvious to me while testing APIs with an AI agent.
The agent could generate a perfect request. Headers, body, authentication—it all looked correct.
But it couldn't actually work with my API client.
It couldn't see my request files.
It couldn't execute the request.
It couldn't inspect the response on its own.
So I ended up copying the generated request into my API tool, running it, copying the response back into the chat, then waiting for the next suggestion.
The AI wrote the request.
I moved the data.
That "copy-paste loop" doesn't feel like much at first, but it adds friction to almost every interaction. And once you're working across dozens of requests, environments, or iterations, that friction becomes the workflow.
A chat interface isn't enough anymore
When LLMs were mostly helping with code completion, this wasn't a huge issue.
Today's coding agents are different.
They're expected to carry out tasks, not just suggest them.
That changes what developer tools need to optimize for.
Adding a chat panel beside an existing workflow isn't enough if the agent still depends on a human to move information between systems.
The more context switching required, the less value the agent actually provides.
The tools that stand out are built around agents
The biggest difference I've started noticing isn't which tool has the smartest model.
It's whether the tool exposes its workflow in a way that an agent can actually understand.
One example is Voiden.
Instead of storing API requests in a proprietary format, Voiden keeps them as plain .void files inside your repository.
It also provides AI Skills for Claude and Codex that teach the agents how those files are structured and how to work with them.
That means the agent isn't guessing from screenshots or waiting for me to paste requests into a chat.
It's reading and editing the same files I'm working with.
The workflow becomes something both the developer and the agent can understand.
The shift I'm starting to see
I think we're entering a new phase of developer tooling.
The question is no longer:
"Does this tool have AI?"
It's becoming:
"Can an AI agent actually use this tool?"
Those are very different questions.
Adding AI to an existing workflow often leaves the human acting as the bridge between systems.
Designing the workflow so agents can work with the same artifacts developers already use removes that translation layer.
That's where the real productivity gains start to appear.
I'm curious whether others are seeing the same trend.
Have you found tools that are genuinely built for AI agents, or are most of them still just adding a chat window on top of the old workflow?
If you're interested in the approach I mentioned, you can check out Voiden at https://voiden.md/.
Top comments (1)
Nice write-up! For devs who deal with messy copied text, TextStow might help — it's a Mac menu bar tool combining clipboard history with prompt templates and text cleanup. Free: textstow.com