Working with AI agents is a bit like learning a new language…

At first, the grammar feels unnatural, then something clicks, and suddenly you’re thinking in it. And just when you feel confident, it throws you a sentence you didn’t expect. At Nybble, we’ve lived that transition, from “trying things out” to actually integrating agents into our workflow, and the path has been equal parts exciting, confusing, and strangely fun.

What made the difference wasn’t just the technology. It was keeping a human centered mindset from day one. That meant not leaving everything in the hands of agents, but letting AI accelerate what humans do best.

Over the last year, we’ve gone from prototypes that looked promising on paper to production ready systems that now support real client work. Along the way, we’ve had wins, stumbles, and moments that forced us to rethink how we build. And the biggest lesson? Experimentation is essential, but it only becomes meaningful when paired with a method. That’s how remarkable work happens: curiosity guided by structure, and innovation grounded in purpose.

Where Things Went Right and Why

One of our smoothest experiences came from a project that involved analyzing hundreds of licenses and generating a functional dashboard. The agents handled the volume effortlessly, connecting dots faster than any human team could have. Within two days, we had a working prototype that would’ve taken a week and a half through traditional development.

The magic wasn’t raw automation. It was structured delegation. This is what human driven, AI accelerated really looks like: smarter teams making better decisions, faster, without losing ownership of the outcome.

This is where Leo’s, our Director of Technology and Innovation,  perspective has been consistent from the beginning. As he puts it: “We discovered early on that agents don’t need perfect instructions. They need stable ground. If your environment is chaos, they amplify the chaos. But if you give them guardrails, clear objectives, and a sandbox that mirrors production, suddenly they become incredibly reliable. That’s what changed the way we approached every experiment after the first few missteps.”

When Things Got Messy

Not every story has such clean edges.

Our migration to Spring is a great example of the opposite scenario: high velocity, low structure. The agents produced plenty of results, but not always ones that were consistent or aligned with long term architecture.

It wasn’t a failure. It was a mirror. The lesson was simple: speed without planning always comes back with interest.

“There was a point where the agents were generating so much code that reviewing became a full time job,” Leo said. “And that’s when it hit us: the problem wasn’t the agent. It was us. We were asking for outcomes without giving direction. After that, we stopped treating the agent like an intern who magically knows everything and started treating it like a collaborator who needs context.”

That moment reinforced something we deeply believe in: technology never replaces responsibility. People own the outcome.

Experimentation Only Works When You Can Scale What Works

Across all these projects, the smooth, the messy, and the downright surprising, we kept seeing the same pattern: experimentation accelerates learning, but systemization accelerates results.

And scaling what works is how you turn good ideas into solutions that matter.

That’s why we now follow three rules whenever we bring agents into a workflow:

Prepare the environment before the prompt. Agents perform best when infrastructure, repositories, and context are already aligned.

Review, review, and review again. Outputs get better when feedback loops are human, fast, and frequent.

Don’t fall in love with the output. Agents are very confident. Treat that confidence as a starting point, not a final answer.

This approach didn’t appear overnight. It emerged slowly, through trial and error. And the moment it all clicked, as Leo explained: “the biggest evolution for us wasn’t technical. It was cultural. At some point, we realized that agents weren’t replacing anything. They were extending our curiosity. Once we allowed ourselves to ask better questions, the systems we built with them grew stronger. It sounds almost philosophical, but it changed everything. The agent becomes as good as the mindset behind it.”

From ‘Let’s Try’ to ‘Let’s Build’

That’s where we stand today. Experimentation still drives our exploration, but documentation, planning, and reflection turn those experiments into tools we can rely on.

Working with agents stopped feeling like a novelty the moment we treated them with the same seriousness as any other part of the stack. They are now a steady companion for repetitive, high volume, or well structured tasks, and a creative sparring partner when we need new ideas.

There are still surprises. There always will be. But as long as curiosity and discipline move together, those surprises become opportunities instead of rework.

Experimentation is still the best way to learn. And when you pair it with a method, agents stop being an experiment and become a real part of the team.

Because at the end of the day, building remarkable technology is never about tools alone. It’s about people, purpose, and creating impact that lasts.