How We Built a Web Agent That Remembers and Learns

Vennapusa Chandra Nihitha Reddy — Sun, 27 Sep 2026 17:27:52 +0000

Building a Web Agent That Learns: My Experience as a Team Member

AI agents are becoming increasingly capable of generating websites, writing code, and completing complex development tasks from simple instructions.

But while working on our project, we noticed an important limitation:

An AI agent can generate something today, but does it remember what it learned yesterday?

That question became the foundation of our project — a web development agent with memory that can learn from user preferences, remember previous decisions, evaluate its own output, and use those experiences in future website generation.

I was fortunate to be part of the team that worked on this project, and this article shares my experience, the idea behind the system, and what I learned while building it.

The Problem We Wanted to Solve

Imagine telling an AI agent:

"Keep the design minimal, avoid excessive animations, and use a black, white, and gold color palette."

The agent can follow those instructions and generate a website.

But when you start a completely new project later, you may have to explain the same preferences again.

This creates a problem with the way many AI workflows operate:

Every interaction starts almost from zero.

We wanted to build something different.

Our goal was to create an agent that could:

Understand user instructions
Identify important and reusable preferences
Store those preferences as long-term memory
Recall relevant memories during future projects
Generate websites using both current instructions and previous experience
Evaluate the generated website
Learn from failures and improve future generations

The idea was to move from simple prompt → generation to a continuous learning workflow.

Our Approach

The system is organized around multiple specialized agents and a memory layer.

The overall workflow looks like this:

User
  |
  | Voice / Text
  v
Orchestration Agent
  |
  +----> Memory Recall
  |
  v
+----------------------+
| Copywriter Agent     |
| Design Agent         |
| Code Generator      |
+----------+-----------+
           |
           v
     Generated Website
           |
           v
+----------------------+
| UI/UX Critic         |
| Functionality Critic|
| Performance Critic   |
| Code Quality Critic |
+----------+-----------+
           |
       Pass / Fail
        /       \
      Pass      Fail
       |          |
       v          v
    Preview    Memory
                  |
               Reflect
                  |
                  v
             Code Revision

One of the most important parts of the architecture is that memory is not simply stored after everything is finished.

Relevant memory is recalled before generation.

That means previous experience can actually influence what the agent creates.

My Experience as a Team Member

Working on this project helped me understand that building an AI agent is much more than connecting an LLM to a prompt.

There are several layers involved:

Understanding the user's intent
Managing the workflow between different agents
Deciding what information is worth remembering
Retrieving the right memories at the right time
Evaluating generated results
Handling failures
Feeding useful feedback back into the system

As a team member, I got to see how these individual components have to work together to create a useful agent.

The most interesting part for me was understanding the difference between storing information and using memory intelligently.

Memory Should Contain Useful Knowledge

We did not want the system to remember every sentence a user said.

For example, if someone says:

"Make this heading slightly bigger."

That instruction may only apply to the current website.

But if a user repeatedly says:

"I prefer clean, minimal interfaces."

that is much more likely to be a long-term preference.

This led us to an important principle:

Store decisions and useful experience, not noise.

The memory layer can retain information such as:

Design preferences
UI preferences
User-specific requirements
Previous generation outcomes
Critic feedback
Recurring mistakes
Useful repair strategies

Recall Before Generation

Another important lesson was that the timing of memory matters.

If an agent generates a website first and remembers the user's preferences afterward, the memory cannot improve that particular generation.

Instead, our workflow recalls relevant information before the generation process begins.

Conceptually:

New Project
    |
    v
Recall Previous Experience
    |
    v
Combine:
Current Prompt + Previous Memory
    |
    v
Generate Website

This allows previous knowledge to become part of the agent's context.

For example, if the agent previously learned that a user prefers minimal layouts and limited animation, those preferences can influence the design of a completely new website.

Learning From Failure

One of the parts I found particularly interesting was the critic loop.

Generating a website is only the first step.

The generated result can be evaluated from multiple perspectives:

UI/UX
Functionality
Performance
Mobile behavior
Code quality

If the generated website does not meet the required criteria, the problems can be retained as experience.

The system can then reflect on those issues and generate repair instructions before attempting another revision.

So the workflow becomes:

Generate
   ↓
Evaluate
   ↓
Problems Found
   ↓
Remember Problems
   ↓
Reflect
   ↓
Generate Again
   ↓
Evaluate Again

This gives the agent another type of memory:

It can remember not only what the user likes, but also what went wrong.

The "Second Project" Test

For me, one of the most exciting ideas in this project is the second-project test.

Imagine teaching the agent your preferences while building Project 1.

Then start Project 2 without explicitly repeating those preferences.

If the agent recalls the relevant information and applies it to the new project, the memory system is actually providing value.

That changes the workflow from:

Project 1 → Generate
Project 2 → Explain Everything Again
Project 3 → Explain Everything Again

to:

Project 1
   ↓
Learn
   ↓
Remember
   ↓
Project 2
   ↓
Recall
   ↓
Generate Better
   ↓
Learn Again

That is the behavior we wanted to achieve.

Technology Behind the Memory Layer

Our project uses Hindsight as the memory layer.

The system can retain useful information and later recall relevant memories based on the current task.

During development, we also considered resilience.

When cloud credentials are not available, the project has a local persistence path that can store learned memories locally. This made development and testing easier while keeping the intended deployed architecture centered around the dedicated memory layer.

What I Learned

Being part of this project taught me several things.

1. AI agents are systems, not just prompts

A powerful prompt alone does not create a reliable agent.

The surrounding architecture — memory, orchestration, evaluation, feedback, and iteration — is equally important.

2. Memory needs a purpose

Simply storing more information does not automatically make an agent better.

The important questions are:

What should be remembered?

When should it be recalled?

How should it influence the next decision?

3. Failure can become knowledge

A failed generation does not necessarily mean the process was wasted.

If the system can identify why something failed and use that information later, failure becomes part of the learning process.

4. Teamwork matters in AI projects

Projects like this involve many moving parts.

Working as a team helped us divide the problem, experiment with different components, discuss failures, and combine individual ideas into a single working system.

For me, that collaborative process was just as valuable as the technical implementation.

From Generation to Learning

The biggest takeaway from this project is the change in mindset.

A traditional AI workflow can look like:

Prompt → Generate → Done

Our goal was closer to:

Interact
   ↓
Learn
   ↓
Remember
   ↓
Recall
   ↓
Generate
   ↓
Evaluate
   ↓
Reflect
   ↓
Learn Again

That difference may look small, but it changes how we think about AI development agents.

Instead of treating every request as an isolated task, the agent can gradually build experience.

Final Thoughts

Building this project as a team member gave me a much better understanding of what it takes to create an AI agent that goes beyond one-shot generation.

The most interesting part isn't simply watching an AI generate a website.

It is watching the system use what it learned from previous interactions to approach the next project differently.

That is where AI agents start becoming more than generators.

They start becoming systems that can learn from interaction, remember useful experience, evaluate their own work, and improve over time.

I'm grateful to have been part of the team behind this project, and I'm excited to continue exploring what memory-enabled agents can do next.

DEV Community: Vennapusa Chandra Nihitha Reddy