Why we're still building autocomplete when the future is agents
The promise of agents is real, but AI adopters need a lower-stakes entry point.
You’re inside a file. Not in a chat window, not reviewing an agent diff—inside a file, cursor blinking, writing a function you’ve written variations of a hundred times.
The suggestion appears, and you hit Tab. Those are 12 words you didn’t have to type yourself. Now you’re back to the problem.
That interaction took 200 milliseconds and cost zero cognitive overhead. And it happened, if you use Kilo Code, somewhere between 50 and 200 times today.
That’s flow.
Why does this matter, when the whole industry is talking about agents?
The promise of agents is real. Multi-step, multi-file, multi-model orchestration is where professional software development is going. At Kilo, we believe that. We’re building toward it—Orchestrator Mode already ships today. In a lot of ways, we’re already living it.
But right now, a massive group of developers—not the early adopters who have restructured their entire workflow, but the huge middle—is experimenting with AI, getting burned, losing trust, and pulling back.
Stack Overflow’s 2025 Developer Survey puts numbers to it: 49% of professional developers still don’t use AI tools daily, and 15% don’t plan to use them at all. And the resistance is highest exactly where you’d want agents to operate: 76% of developers don’t plan to use AI for deployment and monitoring, 69% for project planning.
They’re not moving from Stack Overflow to multi-agent orchestration in one step— nobody is.
They need a low-stakes entry point: something that delivers value in 200 milliseconds. Something where the cost of failure is one misplaced suggestion you reject in under two seconds.
That’s autocomplete.
So what does it actually mean to build great autocomplete?
It means solving the same infrastructure problems that agents require:
Model routing that handles latency at scale
Context construction that understands what’s actually relevant in a 40-file project
Evaluation pipelines that measure acceptance and rejection signals with enough precision to improve the model
Feedback loops tight enough to ship improvements weekly, not quarterly
Every one of these problems is a prerequisite for excellent agents. The companies that skip them—that go straight to the agentic surface without the infrastructure discipline—find out the hard way. If the agent is slow when it should be fast, or hallucinates in the context where it matters most, your developers will lose trust and development will stall.
So, you build the foundation first, then you build on it.
Autocomplete is the on-ramp, not the end goal
We’ve watched this play out consistently: developers who start with reliable autocomplete are far more likely to adopt chat, then agents, then orchestration. Developers whose first AI experience is a broken agent often don’t come back.
A few things I want to be clear on:
I’m not saying autocomplete is more important than agents
I’m not saying the agentic future is far off
I’m not saying every company’s path looks the same
What I am saying is that autocomplete is not the opposite of the agentic future. It’s the on-ramp to it. The developers who will be running multi-agent workflows in two years are, right now, still living in autocomplete land.
Meet them there. Build the infrastructure that earns their trust at 200 milliseconds. Then give them everything above it.
That Tab keystroke is where Kilo Code starts. Come make it better.