Our learnings while building Agentic AI Product from grounds up : Part-1

There’s a saying I’ve heard often: “There are decades where nothing happens, and weeks where decades happen.”

That’s what it feels like to be building in AI right now. Every week, there’s a new model, a new optimization trick, a new architecture. Its very hard to keep up and we are living in a very excited time.

1 year ago, our team dove deep into building AI agents not as a researchers, not as a scientist, but as an engineers trying to make these things actually work.

What we discovered along the way? Most of what people talk about online isn’t the real challenge. The hard parts are quieter, less glamorous, but far more important.

So, I want to share the lessons my team and I have learned not as a textbook, not as a pitch, but as a lived experience. Maybe you’ll find a perspective that helps you avoid a mistake, or double down on what matters. In this part-1 , we will keep it high level “Gyan style”

First, Let’s understand : What’s an “Agent”?

We throw this term around so casually that it almost loses meaning.

For me, an AI Agent is not some futuristic, self-aware robot. It’s much simpler:

Agent = LLM + Tools + Memory

That’s it.

• LLM (the brain)
• Tools (the hands)
• Memory (the history + context)

Everything else you hear , agentic workflows, teams of agents, multi-agent orchestration, are just pipelines of these simple building blocks.

When you strip away the noise, you realize the magic is not in the complexity. It’s in how well you connect these basic components.

To all the non-technical folks who look at this and go blank: print(“\n”.join([f”Line {i}” for i in range(1,11)]))

If this tiny code feels confusing, imagine how tough it is to actually build and run AI agents inside a real product. Writing is hard, running it reliably is even harder. So pls do not discount engineering efforts. The random demo that you saw on LinkedIn or X post is full of fluff and does not work in practice.

Lesson 1: Frameworks Don’t Matter as Much as You Think

We’ve tried (almost) all of them, Langchain, LangGraph , Crewai, AutoGen, OpenAI SDK, Mastra… you name it, we had our hands on it.

And after all that, here’s the unsexy truth: the framework doesn’t matter nearly as much as the pipeline you design. It’s important but not the only thing that matters.

Frameworks are like power tools. They’re useful, but if you don’t know how to build a sturdy table, no tool is going to save you.

So if you’re starting out, don’t stress over which framework is “best.” Instead, ask:

• What’s the workflow I’m enabling?
• Where does the agent start and end?
• How does data move through the system?

That’s where the battle is won.

Lesson 2: Building Agents ≠ Building AI

I call myself an AI engineer. But if I’m honest, most days I feel like a backend developer who happens to be using LLM APIs (although my team is better than me, who puts the silicon to work)

You don’t need a PhD in machine learning to build useful agents. What you do need is rock-solid software engineering fundamentals:

• Clean abstractions
• Clear APIs
• Reliable error handling
• Thoughtful state management

The fancy AI part comes later. If your fundamentals are shaky, your agent will crumble no matter how smart the model is.

Lesson 3: Context is the Real Superpower

One of the biggest misconceptions I see is this:

“Give the LLM a goal, and it will figure it out.”

It won’t.

An agent is only as good as the context you give it. That means:

• Prompts that are sharp, not fluffy
• Tools that are useful
• Memory that’s structured, not messy
• Environment (rare, but when needed, it’s everything)

A bigger model is rarely the solution. A better-structured context almost always is.

Lesson 4: Without Tools, Agents are Useless

A standalone agent that just “thinks”? It gets stuck fast.

The moment you give it tools APIs, databases, workflows it transforms. Suddenly it’s not just reasoning in the void; it’s taking action in the world.

Because here’s the truth: thinking without doing is a dead end.

Lesson 5: Simplicity Beats Complexity Every Single Time

Some of my most effective agents weren’t elaborate at all.

They had:

• A clear prompt
• One or two tools
• A single, sharp responsibility

And they worked beautifully.

Complexity is seductive, but it makes systems brittle. The best agents aren’t built to solve “everything.” They’re built to solve one thing extremely well.

Think of it like a Swiss Army knife vs. a screwdriver. In the real world, you want the screwdriver.

Lesson 6: Evaluation is the Secret Ingredient Nobody Talks About

It’s easy to make a flashy demo. The internet is full of them.

But what separates a toy project from a production system is this: evaluation.

• Do you have real-world test cases?
• Are you running feedback loops?
• Can you measure how well the agent is actually performing?

If you don’t measure, you’re just guessing. And in production, guessing kills trust faster than anything.

Lesson 7: People Matter More Than Tech

This one’s personal.

Working in startups taught me that technology is just one piece of the puzzle. What really matters is the culture around it:

• How fast you iterate
• How open you are to experimentation
• How clearly you communicate vision

An average use case, in the right hands, creates real value. A brilliant use case, in the wrong hands, goes nowhere.

At the end of the day, people build the product, not the other way around.

Where This Leaves Us

AI agents are still early. The hype is real, but so are the challenges.

What excites me most is not the flashy demos. It’s the fact that agents are slowly becoming infrastructure-level systems. In the future, I believe every product will quietly have agents running in the background, coordinating tasks, personalizing experiences, handling the messy complexity humans don’t want to deal with.

And if there’s one thing I’ve learned, it’s this: agents aren’t the product. They’re the enabler.

The real magic happens when they disappear into the background… and just work.

Final Reflection

If you’re building in this space, you’ll make mistakes. You’ll overcomplicate things. You’ll chase the wrong metrics. I certainly did.

But each stumble is a lesson. Each dead end is a redirection. And if you keep at it, you’ll discover something counterintuitive but liberating:

The best agents aren’t the most powerful ones. They’re the most useful ones.

Thats All Folks

I’ve shared my lessons, the raw, imperfect truths I’ve lived through while building. But I know this space is bigger than any one person’s perspective.