The Day Everything Changed……..

I still remember how overwhelmed our team was: repetitive tasks clogging productivity, support tickets piling up, and customers asking for faster responses. It felt like we were constantly firefighting instead of innovating. I knew something had to change — and that’s when we started exploring agentic AI.

At first, the challenge seemed massive. Was this just another buzzword? Or a real solution capable of transforming software workflows? The data showed that intelligent ai systems were becoming business‑critical:

According to recent research, 78% of organizations now use AI in at least one business function, a sharp rise from just 55% a few years ago, signaling that AI has become mainstream across industries. 

With that context, we decided to build our first AI agent — and RubikChat AI agent builder became central to our journey. Here’s how it unfolded step by step.

Step 1: Setting Clear Business Goals & AI Strategy

The first thing I did was define what problem this AI agent must solve. Without business clarity, AI quickly becomes an expensive experiment with little measurable impact — something many companies struggle with.

In fact, while nearly everyone uses AI, only a smaller share has moved beyond experimentation: about 62% are experimenting with AI agents and just 23% are scaling agentic systems across real operations. McKinsey & Company

We knew our goals had to be business‑driven:

• Automate repetitive tasks
• Boost customer engagement
• Reduce operational costs

Deciding early in our AI agent development process whether to use complex Large Language Models (LLMs) or lighter Small Language Models (SLMs) was crucial — not only for maximizing performance but also for managing budget, infrastructure, and long-term scalability.

Step 2: Building the Right Team

No AI project succeeds without people who can execute it. I formed a cross‑functional team:

• AI engineers to design and tune models
• Software developers to integrate the agent into our stack
• UI/UX designers to make interactions intuitive

Where gaps existed, RubikChat AI consulting helped us move faster — a support structure many teams overlook.

This matters because even though AI usage is widespread, only a minority of organizations are seeing measurable bottom‑line impact. Lighthouse AI Enablement

Step 3: Data Collection & Preprocessing — The Heavy Lifting

AI is only as good as the data it learns from. We gathered logs, interaction histories, API data, and user behavior patterns. Then we cleaned, labeled, and anonymized it to ensure privacy compliance.

This was one of the biggest hurdles — but essential for predictive accuracy, reliability, and fairness. For LLM‑based agents, this came down to context‑rich data access, while SLM projects emphasized precision and domain specificity.

Step 4: Choosing the Right Tools, Frameworks & Platforms

We mapped our tech stack based on needs, not trends:

• Frameworks/Libraries: RubikChat, LangChain, Microsoft Autogen, Flowise
• AI Models: LLMs for broad reasoning, SLMs for task‑specific jobs
• Deployment: Cloud for scalability and flexibility

Here’s a snapshot of the decision criteria we used:

RubikChat made integration smoother than expected, letting us focus more on what the agent should do rather than how it should fit in.

Step 5: Building and Integrating the AI Agent

This was an exciting phase. We:

• Created core logic and workflows
• Integrated ML models with APIs and microservices
• Built conversational UI for user engagement

Watching the AI complete tasks autonomously was like hiring a tireless teammate — one that never sleeps and never makes manual errors.

It’s no surprise that many companies are turning to builders like us: research suggests up to 75% of businesses report improved customer satisfaction after deploying AI agents. Warmly

Step 6: Testing & Evaluation — Don’t Skip This

Before going live, we adopted rigorous testing:

• A/B tests against baseline workflows
• Edge‑case simulations
• Latency and accuracy benchmarks

This mirrors industry practice: organizations deploying AI agents often see accelerated performance — but testing is what separates success from hype. As some reports point out, many teams struggle because they treat AI as a shiny add‑on instead of engineering it responsibly. Reddit

Thanks to RubikChat’s testing tools, we caught issues that would have derailed user experience later on.

Step 7: Deployment & Continuous Improvement

Going live wasn’t the end — it was the beginning:

• We tracked performance metrics
• Collected user feedback for iterative updates
• Built a feedback loop so the agent gets smarter with real usage

This mirrors broader industry trends: while AI adoption is near ubiquitous, the real value comes from continuous iteration and scaling rather than one‑off implementation.

Why These Numbers Matter

Here’s the landscape in 2025:

• 78% of companies are using AI in at least one business function — showing widespread acceptance.
   
• 62% are experimenting with agentic AI, with a smaller but growing group scaling it.
   
• 75% of businesses report improved customer satisfaction after deploying AI agents — proving real impact. 

But adoption alone isn’t enough; strategic execution determines who benefits most.

Final Takeaways: 

AI Agents Are Not Optional

Implementing an AI agent changed the way we work. What used to eat up hours of repetitive tasks now happens automatically, giving our team time for high‑value innovation.

If you’re ready to experience the same transformation, there’s no better time than now. With RubikChat AI agents, you can build powerful, scalable, and intelligent systems that grow with your business.

Start building your AI agent today and revolutionize your software operations: