DEV Community: Muhammad Mubashir

How to Build an AI Agent for Your Business

Muhammad Mubashir — Wed, 09 Sep 2026 09:37:23 +0000

Suppose a customer sends a question at 2:47 AM.
Nobody from the support team is online, but the customer still expects an answer. An AI agent can handle that conversation, check the relevant business data using RAG, take an action when appropriate, and hand the conversation to a human when it reaches a situation that needs judgment.
That is what makes AI agents useful for businesses. They can do more than generate text. They can work with tools, follow instructions, access information, and complete specific tasks all on there own.
If you are thinking about building one for your business, the hardest part is usually not choosing an AI model. The harder part is deciding what the agent should actually do.

What is an AI agent?

An AI agent is a software system that can receive a goal, reason about the task, use available tools, and produce an outcome.
A basic chatbot might answer:
"Your order is currently being processed."
An AI agent could:

Identify the customer.
Search the order database.
Check the current order status.
Explain the delay.
Create a support ticket if necessary.
Send the customer the ticket number.

The difference is action.
Start with one business problem
Before writing code, choose a single workflow.
Good starting points include:
Customer support
Lead qualification
Appointment scheduling
Internal knowledge search
Sales follow-ups
Invoice processing
Email classification
Order status checking

For example, imagine a company receives hundreds of support emails every day. Instead of building a general purpose "AI assistant," define a narrower objective:
"Read incoming support requests, identify the issue, retrieve relevant customer information, suggest a response, and create a ticket when human assistance is required."

The Architecture

A business AI agent usually contains several parts:
USER, APP, AGENT, DB, API, Knowledge. The AI model handles reasoning and language. Your application controls permissions, authentication, business rules, logging, and access to external systems. Tools allow the agent to perform actions. The database and knowledge base provide information.

Step 1: Define the agent's job

Write the agent's responsibility in one paragraph.
"The support agent helps customers check orders, answer common product questions, and create support tickets. It must ask for clarification when required information is missing and transfer the conversation to a human when it cannot safely resolve the issue."
Then define what it should not do.
It cannot issue refunds.
It cannot modify customer accounts without authorization.
It cannot expose internal customer information.
It cannot invent order information.
It cannot approve exceptions.

Step 2: Give the agent access to tools

An AI agent becomes useful when it can interact with your existing software.
Suppose your business already has an API: GET /api/orders/{orderId}
Agent uses = get_order_status(order_id)
The model decides when that tool is needed.
Your backend executes the actual API request.
Customer:
"Where is order #48291?"
AI:
I need the order information.
Tool:
get_order_status(48291)
API:

{
  "status": "shipped",
  "estimated_delivery": "September 12"
}

AI:
"Your order has been shipped and is expected to arrive on September 12."

Step 3: Connect your business data

An agent needs reliable information.
Structured data
This includes:
PostgreSQL
MySQL
CRM systems
Payment systems
Order management systems

Use structured queries or APIs when the agent needs precise information.
Unstructured data
Like:
PDFs
Product manuals
Company policies
Documentation
FAQs

This is called Retrieval Augmented Generation, or RAG.
The basic process is:
Documents
↓
Split into chunks
↓
Create embeddings
↓
Store in vector database
↓
User asks question
↓
Retrieve relevant chunks
↓
AI generates answer

Step 4: Choose your technology

You do not need a huge stack to build your first agent.
Frontend: Next.js Backend: Nestjs, AI: LLM API, Database: PostgreSQL Vector Search pgvector, Authentication: JWT / OAuth, Deployment: Docker
If you already know JavaScript and Node.js, building the backend with Node.js can reduce the amount of new technology you need to learn.

The system instructions should clearly define:
Its role
Available tools
Rules for using tools
Information it can access
Actions it can perform
Situations requiring human escalation
How it should handle uncertainty

Step 6: Add memory carefully

An agent may need conversation context.
The agent needs the earlier message to understand what "my order" refers to. You can store conversation history in a database and provide the relevant context to the model. Long conversations create another problem: too much context increases cost and can make responses less reliable.
A better design can summarize older conversations while keeping important structured information separately.
Step 7: Add human approval for sensitive actions
Not every action should happen automatically. Consider an agent that can issue refunds. A safer flow is Customer requests refund, AI checks order, AI determines refund may be valid, then Human approval, then a Refund API hit. For low risk operations, full automation may be reasonable.
For financial transactions, account deletion, legal decisions, or other high-impact actions, human approval can provide an important safety boundary.

Step 8: Build Observability from day one

When an agent makes a mistake, you need to know why.
Log information such as:

Request ID
User ID
Agent decision
Tools called
Tool arguments
Tool results
Model response
You can then investigate questions like: Which tool did the agent call? Did the tool return incorrect data? Did the model misunderstand the result?

Step 9: Test real scenarios

A successful demo does not mean your agent is ready for production.
Create a test dataset containing normal and difficult cases.
Step 10: Start small and expand
A common mistake is trying to build an agent that handles everything.
Start with one workflow.
For example: Version 1
Check order status Version 2
Answer product questions Version 3
Create support tickets, Version 4
Handle appointment scheduling, Version 5
Add human approval workflows
That is much easier to manage than building a large autonomous system before you know how customers will actually use it.
The security layer matters
Giving an AI agent access to business systems creates new security risks.
Your system should consider:

Authentication
Authorization
Rate limiting
Input validation
Prompt injection
Sensitive data exposure
Tool permissions

How much does an AI agent cost?
The cost depends on several variables:
Number of conversations
Model used
Prompt size
Conversation history
Number of tool calls
Retrieval operations
Database usage
Hosting
Monitoring

A small internal agent can potentially run at a relatively low cost.
A customer-facing agent handling thousands of conversations requires more careful cost planning.
Track cost per task rather than only looking at your total monthly AI bill.

Final thoughts

Building an AI agent starts with a business process.
Choose a task, define its boundaries, connect the right tools, give it access to reliable information, and make your backend enforce every important permission.
Then test it with real scenarios and measure whether it actually improves the workflow.
The most useful agent is usually the one that solves a specific problem reliably. Once that works, you can give it more responsibilities.
If you want to build an AI agent for your business contact us

Why Most Products Fail and How an MVP bring Success

Muhammad Mubashir — Mon, 24 Aug 2026 13:03:40 +0000

Why Most Products Fail and How an MVP bring Success

The smartest founders don’t try to build everything. They build the right thing first.

Every year, thousands of new products enter the market. Yet only a fraction become successful. Many promising startup businesses disappear within their first year of launch, despite having talented founders, strong ideas, and significant investment.

So what separates companies like Netflix and Google from the countless startups that never make it?

It isn't always the size of the idea.

It is how effectively the company turns an idea into something customers actually want.

That is where the Minimum Viable Product (MVP) becomes critical.

The Problem: Building Too Much, Too Soon

One of the biggest mistakes startups make is over engineering.

An idea feels exciting, so the developers starts building it and start adding new features into it. Complex systems are build which sometimes take months and even years! invested behind closed doors.

Then comes launch day.

And customers don't care. The founder's get disappointed and quit.

The problem isn't necessarily that the product was poorly built. It's just solving the wrong problem, targeting the wrong audience, or offering features customers never needed.

This is the danger of building based entirely on assumptions with any feedback from the real user.

Your vision may be brilliant. But until real users interact with your product, it's still a hypothesis.

An MVP Is More Than a Simple Product

The term Minimum Viable Product is often misunderstood.

An MVP isn't a basic, draft of your final product. It is specifically designed to answer one important question:

Will customers actually find this product valuable?

The goal is to identify the smallest set of features needed to solve a specific customer problem and put that solution in front of real users as quickly as possible.

Instead of spending a year building 50 features, a startup might build five.

Instead of guessing what customers want, the team watches how they actually use the product.

Instead of treating the original business plan as fixed, the team learns, adapts, and improves.

That's the real power of an MVP.

It's a cycle:

Build -> Launch -> Feedback -> Measure -> Learn -> Improve -> Repeat

Your first version doesn't need to be perfect.

It needs to be useful enough to generate meaningful feedback.

That feedback can reveal things your internal team could never predict.

Customers may use a feature differently than expected. They may ignore your most expensive feature entirely. They may request something you never considered. Or they may reveal that the problem you're solving isn't painful enough to make them pay.

Customer Satisfaction is the Key

All of these outcomes are valuable.

Because discovering a wrong assumption after four weeks is far better than discovering it after four years.

Why Product Strategy Matters

Technology alone doesn't guarantee product market fit.

You can have an excellent engineering team and still build the wrong product.

That's why MVP development should begin before writing thousands of lines of code.

Founders need to answer questions such as:

Who exactly is the customer?
What problem are we solving?
How painful is that problem?
What solution would customers actually pay for?
Which features are essential?
Which features can wait?
What assumptions need to be tested first?
What does success look like?

These questions create clarity.

And clarity prevents unnecessary development.

This Is Where SolveMotive Comes In

We help founders build their businesses according to MVP principles
Rather than just bloating with complex uneccessary requirements

The objective is not to build the biggest possible product.

It's to build the right product first.

By combining product strategy, custom software development, and engineering expertise, SolveMotive helps founders identify the core problem, and create an MVP that can evolve as the market provides feedback.

You can explore our product strategy and MVP development approch at

SOLVEMOTIVE

Your First Product Doesn't Need to Be Your Final Product

Your ultimate product may eventually have dozens of features, sophisticated automation, powerful AI capabilities, and millions of users.

But your first version doesn't need all of that.

It needs to prove something. Something that users are facing the problem of, something that can be improved in the society, something that real users would genuinely pay for

The Real is Faster Learning

Startups rarely have the resources of established companies.

They can't always compete on advertising budgets, infrastructure, brand recognition, or headcount.

But they can compete on speed of learning.

Final Thought

The biggest startup mistake isn't launching a product that isn't perfect.

It's spending too long building something without knowing whether people actually want it.

An MVP fixes this exact issue. It turns product development from just guessing into actually improving process.

Build less.

Talk to customers sooner.

Measure what matters.

Learn from the market.

Then build again.

Solid Principle in OOP

Muhammad Mubashir — Mon, 13 Jul 2026 22:21:13 +0000

Hello World!

Muhammad Mubashir — Mon, 23 Mar 2026 06:41:59 +0000