Beyond Generation: Building a Web Agent That Learns From Its Developer

Sodhan krishna sai — Sun, 27 Sep 2026 17:32:58 +0000

I Was Part of Building a Web Agent That Learns From How We Build

AI coding agents are getting surprisingly good at generating websites.

Give them a prompt, and they can produce a landing page, write HTML and CSS, add interactions, and even structure an entire project.

But while working on our project, we kept coming back to one question:

What happens when the agent has to build the next project?

Does it remember what the developer prefers?

Does it remember what went wrong before?

Can it improve instead of starting from zero every time?

That question became the foundation of the web development agent our team built.

My teammate Satya Harsha Yadavalli has already written about the technical architecture and the memory implementation. I wanted to share the project from another perspective — what we were actually trying to solve as a team and what I learned while building it.

The Problem We Wanted to Solve

One of the frustrating things about AI coding tools is that a lot of context disappears between sessions.

Imagine telling an agent:

"Keep the design minimal."

Then later:

"Don't use gradients."

And later:

"I prefer subtle animations."

The agent may follow those instructions perfectly in that session.

But when you start another project, you often have to explain the same things again.

That creates a strange situation where the developer is effectively becoming the agent's external memory.

We wanted to reverse that.

Instead of:

Prompt
  ↓
Generate
  ↓
Done

we wanted something closer to:

Interact
   ↓
Learn
   ↓
Remember
   ↓
Recall
   ↓
Generate
   ↓
Evaluate
   ↓
Improve
   ↓
Learn again

That change in thinking made the project much more interesting.

More Than Just a Website Generator

Our system isn't simply an LLM that receives a prompt and returns HTML.

The workflow contains multiple stages.

The user can provide instructions through text or voice. An orchestration layer then coordinates different agents responsible for things such as:

Copywriting
UI/design decisions
Code generation
UI/UX evaluation
Functionality evaluation
Performance evaluation
Code-quality evaluation

The generated website is evaluated before being treated as finished.

If problems are found, the system can feed those problems back into the workflow and attempt another iteration.

The memory layer is powered by Hindsight, which allows the system to retain and recall useful information.

And this is where the project became different from a normal AI website generator.

The Interesting Part: Memory

At first, "adding memory" sounds simple.

Just save the conversation somewhere.

But that isn't really useful.

If an agent remembers every sentence ever written to it, eventually the memory becomes noisy.

For example:

Make this heading bigger.

is probably not something that should become a permanent preference.

But:

I prefer minimal interfaces without excessive animation.

could be useful across future projects.

So one of the ideas we explored was separating temporary instructions from durable preferences.

The agent can identify useful preferences and retain them as long-term information.

Later, when another project starts, the system recalls relevant memories before generation begins.

That "before" is important.

Memory isn't very useful if the website has already been generated.

The recalled information needs to influence the generation itself.

The Second Project Is the Real Test

One of my favorite parts of the idea is that the first project isn't really the best demonstration of memory.

The real test is the second project.

Imagine:

Project 1

You tell the agent:

Keep the interface minimal.
Avoid gradients.
Use subtle animations.
Prefer a particular visual style.

The agent learns these preferences.

Project 2

You don't mention any of those things.

The agent recalls the previous preferences and uses them while generating the new website.

That's when memory becomes visible.

The interaction changes from:

"Remember this."

to:

"I don't need to tell you this again."

That was one of the core ideas behind our project.

The Agent Can Remember Mistakes Too

Another part of the system that I found particularly interesting was the critic loop.

The agent doesn't only need to remember what the developer likes.

It can also learn from what went wrong.

After generating a website, different critics evaluate it.

For example:

Generated Website
       ↓
 ┌───────────────┐
 │ UI/UX Critic  │
 │ Functionality │
 │ Performance   │
 │ Code Quality  │
 └───────┬───────┘
         ↓
      Evaluation
         ↓
   Pass? ─── No
            ↓
       Store Issues
            ↓
         Reflect
            ↓
       Revise Code

This creates another form of memory.

Instead of only remembering:

"The developer likes X."

the system can also retain:

"This approach caused Y problem."

That distinction is important.

The agent isn't just remembering preferences.

It is accumulating experience.

What I Learned From the Project

The biggest lesson for me was that an AI agent isn't necessarily made more useful by simply giving it more information.

The useful part is deciding:

What should it remember?

When should it remember it?

When should it recall it?

How should that memory affect its next decision?

That led me to think about memory less like a database and more like an experience layer.

A database can tell you:

What was stored?

A useful agent memory system should help answer:

What matters?

When does it matter?

How should it affect the next action?

That difference is small in wording but huge in system design.

Building This as a Team

One thing I particularly enjoyed about this project was that it wasn't just about getting an LLM to generate something impressive once.

We had to think about the complete workflow:

User
 ↓
Instruction
 ↓
Orchestration
 ↓
Memory Recall
 ↓
Specialized Agents
 ↓
Code Generation
 ↓
Critics
 ↓
Reflection
 ↓
Revision
 ↓
Memory

Each part affects the next.

A problem in one stage can create problems downstream.

That made the project feel much closer to engineering an actual system than simply writing a prompt.

Where This Could Go

The current project focuses on web development, but the underlying idea can go much further.

Imagine an AI development environment that gradually understands:

Your coding conventions
Your preferred architecture
Your UI preferences
Your debugging patterns
Common mistakes in your projects
Previous solutions that worked
Previous solutions that failed

Instead of every AI session being a clean slate, the agent could become increasingly familiar with the way you work.

The goal isn't to make the AI "know everything."

It's to make it remember the things that actually matter.

Final Thought

When we started, the obvious goal seemed to be:

Build an AI that can generate websites.

But the more interesting goal became:

Build an AI that can get better at working with its developer over time.

That difference completely changed how I looked at the project.

The first generated website is impressive.

The second website remembering what happened during the first one is where the real idea starts becoming interesting.

And that's the direction I think AI development agents are going toward:

Generate
   ↓
Learn
   ↓
Remember
   ↓
Improve
   ↓
Build again

We are still experimenting with what this kind of persistent agent memory can become, but building this system gave me a much better understanding of what it means for an AI agent to actually learn from interaction instead of simply responding to it.

DEV Community: Sodhan krishna sai