I've been building developer tools for five years.
For the first three years at DhiWise, we were automating
one thing: turning Figma designs into production code.
Pick a framework (Flutter, Kotlin, React), upload your
design, get clean code out. That was the whole product.
Then everything changed.
The Pivot That Changed How I Build
In 2023 we launched WiseGPT -- a context-aware AI coding
assistant that plugged into VS Code and understood your
local codebase. No prompt engineering. Just describe what
you want, and it generates code that fits your actual
project structure.
That was my first real exposure to building AI agent
systems in production. Not demos. Not prototypes. Real
agents, serving real developers, at scale.
By 2025, the company rebranded to Rocket.new and the
product became something much bigger: an AI-first
application builder that generates complete, full-stack,
production-ready applications from plain English prompts.
I'm one of the engineers building the agent systems
behind that.
What an AI Agent System Actually Looks Like in Production
A lot of tutorials show you a single LLM call. You send
a prompt, you get a response. That's not an agent system.
That's autocomplete.
At Rocket.new, a single user prompt like "build me a
SaaS landing page with a waitlist form" triggers a
coordinated pipeline of specialized agents:
- A research agent that analyzes the request and identifies what components are needed
- A design agent that makes UI/UX decisions
- A code generation agent that writes frontend, backend, and database logic
- A validation agent that checks the output for errors before it reaches the user
These agents don't run sequentially. They run in
parallel where possible, coordinate decisions through
a shared context layer, and hand off state to each
other asynchronously.
The Hardest Problem: Context Engineering
The biggest technical challenge isn't the LLM calls.
It's context engineering -- deciding what information
each agent needs to do its job well, and making sure
that information is available at the right time in the
right format.
Too much context and the model gets confused. Too little
and it makes wrong assumptions. The art is in building
the pipelines that route exactly the right context to
each agent at each step.
This is what I spend most of my time on at Rocket.new.
What I've Learned Building This
1. Hybrid architectures beat pure LLM systems
Combining utility-based decision making (deterministic
rules) with LLM-based reasoning gives you systems that
are both reliable and flexible. Pure LLM systems drift.
Hybrid systems are controllable.
2. Error cascading is the real enemy
When one agent makes a small mistake, the next agent
inherits that mistake and builds on it. By the time
you're three agents deep, you have a completely wrong
output. The solution is validation gates between agents,
not just at the end.
3. Latency matters more than accuracy at first
A faster, slightly less perfect answer beats a slow,
perfect one. Users lose trust when they wait. Build for
speed first, then improve accuracy incrementally.
The platform now serves 400,000+ developers across 180
countries and has generated 500,000+ production-ready
applications.
If you're building AI agent systems and want to trade
notes, I'm always open to it.
Find my other technical writing at https://www.rocket.new/author/dhruv-gandhi.
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