How Eximus started adopting OpenClaw

A few months ago, OpenClaw was everywhere. There was excitement, demo energy, and a lot of conversation around agents. What caught our attention at Eximus was not only the conversational layer. It was the idea of having a system that could answer, execute commands, and complete real tasks with some operating latitude.

That changes the discussion immediately.

At that point, this is no longer only about text generation. It is about software that can act inside a real technical environment.

That interested us quickly, but under one condition: test it without improvising the whole foundation.

Infrastructure was not the bottleneck

At Eximus, we had already spent time bringing order to our Azure base. We still rely heavily on Azure IaaS, we had been moving infrastructure into Terraform, and we already had an Azure DevOps repository with a meaningful portion of provisioning described as code.

That made a concrete difference when we decided to move.

The first step was to create a dedicated Linux VM for agents. Because the infrastructure pattern already existed, provisioning a new resource was not a long adventure. It was a relatively short step inside an architecture that already had some discipline.

That freed time for the questions that actually mattered: how to isolate it, how to connect it to the rest of the company, and how to reduce risk from day one.

If the system can act, security stops being decorative

From the start, we knew this environment could not be left publicly exposed like another demo. Showing an interface is one thing. Operating a system that can run commands, inspect files, and become a practical layer of internal work is something else entirely.

So the server was left without direct public access. We placed it behind a reverse proxy and kept it functional only inside our private Azure VNET. Administrative access was restricted to VPN and SSH keys.

That may sound obvious, but it is worth stating plainly: when you deploy agents with operating capability, security cannot be pushed to the end. The perimeter, access paths, segmentation, and exposed surface become part of the product design, not just part of the infrastructure checklist.

The next challenge was making it useful for the company

Running OpenClaw on a VM solved the technical base, but not the real adoption problem. The practical question was different: how would the team actually use it day to day?

Eximus operates heavily on Microsoft tooling, and Microsoft Teams is our natural coordination channel. So integrating with Teams was not a nice extra for later. It was a real condition for OpenClaw to stop being an isolated experiment and start feeling useful inside the company.

That is where the work became more interesting.

Integrating Teams was far more tangled than it looked

On paper, the story sounds simple: register the application in Azure, prepare the bot, expose a secure endpoint, and connect the channel.

In practice, there were several moving parts to line up before it behaved reliably. We relied on IaC again to prepare the Azure-side resources, but we also had to resolve the Teams-side bridge and the way that channel would talk to the OpenClaw runtime.

Without getting lost in unnecessary detail, the challenge was to align three planes at the same time:

• the application and permissions on the Microsoft side;
• the secure endpoint and runtime on our side;
• and a bridge capable of moving events, requests, and responses between them.

That is where less glamorous but decisive issues show up: messages, conversation state, websockets, handoffs between components, and the friction that appears when platforms were not originally designed for this kind of agent experience.

That stretch took significantly more work than it seemed at first. And that is normal. Real integration with an enterprise channel rarely comes together cleanly on the first pass.

The most striking part: OpenClaw started helping configure itself

One of the strongest moments in this story came once the environment was minimally functional.

OpenClaw started helping us review part of its own configuration.

As we tuned the system, we used it to inspect proxy settings, fire requests from both sides, compare responses, detect friction, and support the stabilization of the Teams channel. It stopped feeling like a static component you simply deploy. It started behaving like a tool that, under supervision, could collaborate in the same technical work required to make it stable.

That was a strong signal. The promise was no longer theoretical. It was beginning to show up inside the operation itself.

Running the service was only part of it; it still needed a brain

Once the service was running on the agents server and the main integration path was in place, another equally important decision appeared: which models it would use and under what limits.

At that stage, the initial stack was deliberately pragmatic. We had an OpenAI Plus subscription, a Claude subscription, and the company OpenAI API with a controlled budget. That was enough fuel for the system to start working.

The logic was simple: combine routes according to availability, limits, and suitability for each flow. In theory, that sounds straightforward. In practice, it means living with real cost ceilings, usage caps, continuity issues, and changing consumption conditions.

That became another useful lesson. When you operate agents seriously, the question is not only which model looks best in the abstract. It is also which one proves usable, stable, and sustainable inside day-to-day work.

Early adoption was worth it because it forced us into reality

The value of adopting OpenClaw early was not being able to say we were trying agents. The value was reaching the questions that actually matter:

• how to isolate a system with operating capability;
• how to provision it quickly without losing control;
• how to connect it to the real channel where the team works;
• how to keep it inside a secure architecture;
• and how to combine models with concrete budget and availability limits.

That kind of learning appears when the tool leaves the demo and enters an environment with real constraints.

The longer part of the story came next

This first stage is about infrastructure, security, integration, and the base model stack. But OpenClaw does not become meaningful just because it answers through Teams or because models are connected.

The longer phase came afterwards: defining roles, building memory, organizing coordination between agents, hardening automations, and turning the system into a more serious operating layer inside Eximus.

That second part deserves its own article.

Closing

If I had to reduce what allowed Eximus to adopt OpenClaw relatively quickly, I would put it this way: we already had a serious base in Azure, IaC, security, and technical delivery. That gave us a real environment to test agents with control, not only with enthusiasm.

That was the difference.

Once OpenClaw entered that environment, the conversation stopped being theoretical. The real work began.