How to build AI Agents that scale: From pilot to ecosystem

How to determine which use case is the right starting point for an AI agent to bring the greatest impact?

It's where most projects either set themselves up for success or fail right from the start.

When you’re choosing the first use case, you want to look for something that’s high-volume and repetitive, where the inputs and outputs are pretty clear. Those processes are easier to model, and they tend to deliver value quickly.

Pay attention to where there’s real operational friction, like places where teams are spending hours doing manual work, passing information back and forth, and double-checking the same data. If people are burning time just to keep the process moving, that’s a strong signal.

Another good filter is a process that affects multiple people. If an agent helps an entire team or a cross-functional workflow, the value becomes visible very quickly.

There’s also the data question, of course. An agent is only as good as the data it receives, so it’s smart to start where the data is already clean, accessible, and sitting in one place.

You also want the workflow to be stable. If the process changes every week, the agent will constantly need rework. So the first win should be something well understood and documented.

And maybe the most important point: pick something measurable. Choose a task where success can be quantified — like time saved, cost reduced, or errors eliminated. So you can go back to leadership with a clear proof point for scaling.

One more thing — don’t start with a mission-critical process. The first agent should be meaningful for sure, but not so high-risk that small inaccuracies create big consequences.

Case: Automated AI-driven report generation

Here’s one of those cases where the need for automation was very obvious.

The company delivers due diligence reporting services for the property market. And their team was overwhelmed as volume grew, spending hours creating document summaries. And the leadership realized that unless something changed, they would either fall behind or have to scale the team unnecessarily.

The challenge wasn’t “we need AI” — it was “our analysts are stuck doing repetitive work.” The process behind that was predictable, repetitive, and low-risk — an ideal first use case.

We built an LLM-based AI agent that processes those documents, extracts the required information, and generates structured summaries. This drastically reduced manual effort and sped up report generation.

This solution was a low-budget project that delivered an immediate impact. And it showed that starting small with a clear, repetitive task is often the best way to begin integrating AI.

How should leaders weigh the trade-offs between going with a quick Low-Code build or investing in a custom solution with more control and integration?

Low-Code solutions are a fantastic tool when you’re trying to move quickly and validate the whole idea. They let you get an agent into production fast, test it on some real data, and actually see where the use case delivers enough value to justify going further. You also avoid the upfront technical overhead because the platform handles a lot of basics for you, like hosting and orchestration.

But in the meantime, Low-Code has limits. It works really well for contained workflows, but as soon as you need deeper customization, more complex business logic, or integration across several internal systems, it starts to get restrictive. And that’s usually the point where custom development becomes the better option.

First of all, custom builds give you control over integrations, security, data flow, and scalability — all the things that matter once the agent becomes part of a core workflow. And for companies in regulated industries, the transparency you get with a custom solution can actually be a major deciding factor.

Another thing to keep in mind is the cost structure. Low-Code is inexpensive at the beginning, but can become costly as usage grows or as you need more advanced capabilities. Custom development requires more investment upfront, but it often pays off long-term because you aren’t tied to platform pricing or limitations.

Low-Code is perfect for early validation, and custom development becomes essential when you’re ready to scale and integrate. They’re not competing approaches — they’re different stages of the same journey.

What are the key metrics that actually convince business leaders that the agent is worth scaling?

When we talk about measuring success, the focus really has to shift from activity to outcomes that tie directly to business KPIs — essentially, outcomes that demonstrate real return on investment. It doesn’t mean how many tasks the agent completed — it means what actually changed for the business because of it.

For example:

• Did we shorten the time it takes to make a decision? Maybe a process that used to take three days now takes four hours.

• Did we reduce costs — either within a specific process or across an entire department?

• Maybe we managed to cut down on errors, like eliminating manual invoicing mistakes or improving compliance. That has real financial and reputational value.

• Or did we drive revenue in some way? Like, through more accurate pricing or sharper forecasting.

One of the clearest signs that an agent is working is when other departments start asking for the same thing. It tells you that the value is real and the solution is worth scaling.

Case: Conversational Business Intelligence

WaveAccess’s client relied heavily on dashboards for working capital management. As data grew, the dashboard became harder to extend and adapt and extracting some insights became a real challenge.

We introduced an AI-powered conversational agent integrated into their business intelligence platform into their dashboards. The agent allows users to ask questions in natural language and get answers, insights, and even data visualization instantly.

Instead of tracking queries processes, we looked at business outcomes, like time to insight was reduced from days to seconds. The team no longer needs to create new dashboards manually, and operational costs in the BI department were reduced by 20%.

This shows that focusing on metrics that align with business goals can turn a simple pilot into a scalable solution.

Can an agent be the first step into AI or does a company need a certain level of readiness before?

You can start with an AI agent early — but you can’t scale without a solid foundation.

Agents rely on a strong operational base. That means clean, structured data, systems that can talk to each other, and well-defined processes. If your data is scattered or if the underlying workflow isn’t clear, the agent will either struggle or require constant manual correction.

Agentic AI isn’t a higher level of AI, it’s an orchestration layer that sits on top of whatever systems and data you already have. But that layer only works if what’s underneath is coherent.

In fact, for some companies, building an AI Agent is the fastest way to prove value. But if the goal is to build more agents later, or connect them into a multi-agent workflow, you really need that foundation to be solid — otherwise your first agent becomes isolated and very hard to extend.

How do you move from a single automation to something more powerful, and at what point does the company actually need it?

A single agent is usually focused on one very specific task — such as answering questions, generating content, or routing requests. For a lot of companies, one well-built agent already creates noticeable value.

But eventually, you reach a point where automating a single task isn’t enough.

The real efficiency comes when you’re automating an entire workflow, and that’s where multi-agent systems make sense.

There are a few clear signals to understand when your company truly needs multiple agents:

• The process has distinct steps owned by different roles.

  If a workflow involves review, enrichment, validation, routing, and then reporting, no single agent can do it all. As each step requires different logic or context.

• Tasks depend on different systems or data sources.

  One agent might specialize in the knowledge base, another in ticketing, another in analytics. When one task requires several tools, distributing the work across agents is more stable.

• Business needs parallelization, as the volume increases.

  A single agent becomes a bottleneck, while multiple specialized agents can divide the workload.

• Business wants traceability.

  Multiple agents create cleaner responsibility boundaries — “this agent enriches,” “this one validates,” etc.

• Moving from “answering” to “orchestrating.”

  That’s the shift from task automation to process automation

In a multi-agent setup, each agent does what it is best at, and they communicate, pass context, and call each other when needed — almost like a real department. The real value comes from this coordination.

Case: AI-enhanced support automation

Here’s a good example of multi-agent collaboration from the IT support department of a large bank.

They needed to streamline support operations while improving quality and efficiency.

We created a system where multiple agents work together:

• One agent monitors unanswered customer questions, ensures they are routed to specialists, collects responses, and updates the knowledge base to always keep it up-to-date.

• Another agent searches the knowledge base and provides accurate answers in chat format.

• A third agent automatically creates tickets when no relevant answer is found, ensuring no request is lost.

It was a coordinated workflow. The agents functioned almost like a digital support team, improving response time, accuracy, and overall efficiency.

From the tech perspective, how to keep AI agents aligned and communicating smoothly as the system grows?

It all comes down to a common standard, which is what MCP provides. It’s becoming a pretty widely discussed term in the AI space, and for good reason.

In practical terms, it’s a very effective way to organize and manage how multiple agents work together and communicate with each other.

In a nutshell, a Model Context Protocol is a standard that lets several AI agents operate inside one environment, each with access only to the data and tools they’re supposed to use. It doesn’t replace existing platforms — it just connects the agents so they can collaborate smoothly.

In the case above, MCP allowed the three agents to function in a single chat interface, which made the whole system easier to manage, more secure, and much more scalable as the support workload grew.

How can leaders encourage invention and experimentation without ending up with a disconnected zoo of agents?

Once teams see what a well-designed agent can do, enthusiasm skyrockets. Every department wants their own agent, often built quickly, independently, and without a shared blueprint. One successful agent becomes three, then ten, and they all can be built differently, and they can be pulling from different data and unable to talk to each other. Suddenly, the organization has a collection of agents rather than a real ecosystem.

The key point is that the solution isn’t to restrict invention — it’s to guide it with proper governance.

One of the most effective approaches is establishing a Center of Excellence. It doesn’t have to be a big team but they set the standards for the entire organization. They review new agent proposals, maintain a registry of existing agents, and make sure everything aligns with the company’s architecture, data governance, and security requirements.

The second piece is creating very simple early rules. Things like:

• Which data models to use

• What API standards agents should follow

• How authentication works

• And where logs and monitoring should be centralized

These don’t slow teams down. They actually speed things up because everyone is building on the same foundation instead of reinventing it.

And finally, stick to the “citizenship mindset.” Instead of thinking, “Can we automate this task?” the better question is: “Could this agent eventually collaborate with others?”

If every agent is built with the expectation that it might work with other agents in the future, you naturally get better alignment, better integration, and a much clearer path to scaling into a true multi-agent ecosystem.

To sum it up, encourage invention, but make sure there’s a shared backbone that keeps every new agent from becoming an isolated experiment.