How to Properly Deploy AI Agents to Production

Imagine: a team of developers has created its own AI assistant – with its own logic, tools, and personality. Let's call it, as in Red Hat's original example, OpenClaw. It can work with code, answer questions, and assist with tasks. Everything is fine – until the question arises: How do you deploy it in a real corporate environment? Securely, predictably, and with the ability to track what it's doing?

This is where the story begins for the approach Red Hat calls BYOA – Bring Your Own Agent.

Challenges of Deploying AI Agents

So, What's the Problem?

AI agents are more than just chatbots. Simply put, they are programs that not only answer questions but also perform actions: calling external services, working with files, executing code, and making intermediate decisions. They become part of workflows.

And this is where a dilemma arises, familiar to any organization that is starting to seriously use AI. On the one hand, teams want to create agents the way they see fit: choosing their own frameworks, models, and architecture. On the other hand, IT and security teams want to know what the agent is doing, who controls it, how to update it, and how to stop it if something goes wrong.

The traditional solution to this conflict is standardization. Everyone must use a single corporate framework, wrapping everything in approved wrappers. This is convenient for control but inconvenient for developers: flexibility is lost, overhead increases, and an already written agent has to be reworked.

Red Hat proposes a different path.

The BYOA Approach Explained

The Agent Remains Itself

The key idea of BYOA is that the agent doesn't need to be rewritten to fit the platform's corporate standards. It can be written with any preferred tool, use any model, and have its own internal logic – while still operating within a managed infrastructure.

In Red Hat's publication, OpenClaw serves as a perfect example of this. It's a demonstration agent built without being tied to any specific platform framework. It is taken «as is» to show how Red Hat AI can accept it, wrap it in the necessary operational context, and make it suitable for corporate use – without touching its internal logic.

What does this mean in practice? The platform takes on several tasks that the agent's developer usually doesn't want to handle:

• Security. Who can run the agent, with what permissions, and which resources it can access – all of this is configured at the platform level, not hardcoded into the agent.
• Observability. What the agent did, what requests it sent, how long operations took – the platform collects this data. This is important not only for debugging but also for meeting corporate compliance requirements.
• Lifecycle Management. Deployment, updates, rollbacks – these are standard operations that DevOps teams know how to perform with any application. The agent becomes just another manageable object.
• Policies and Governance. Behavioral restrictions, activity logs, audits – everything needed when an agent is running in a real system, not a sandbox.

Why the Platform Adaptation is Key

Why It's Not Just a Wrapper

An important nuance: Red Hat explicitly emphasizes that this isn't about «wrapping» the agent in a proprietary framework. This is a matter of principle.

If the approach were to take an agent and force it to work through a specific internal layer of the platform, developers would once again find themselves in a state of dependency. One platform today, another tomorrow, and the agent would need to be adapted each time.

Instead, the idea is for the agent to interact with the environment through open, standard interfaces. The platform adapts to the agent, not the other way around. This changes the balance of power: the team that created the agent retains control over its logic, while the platform is responsible for the operational layer.

Benefits of BYOA for Stakeholders

Who Needs This and Why

From the perspective of different stakeholders, the picture looks something like this:

For agent developers – it means freedom. There's no need to learn a specific corporate framework for an agent to work in production. They can use familiar tools and focus on the agent's logic itself.

For IT and security teams – it means manageability. The agent is visible; it can be stopped, updated, and audited. It doesn't operate in a gray area where it's unclear what's happening or who is responsible.

For the organization as a whole – it means a lower barrier to adoption. If every new agent requires complex integration and approvals, initiatives stall. If an agent can be «brought along» and connected to the infrastructure without being rewritten, the process accelerates.

The Future of Corporate AI Agents

The Broader Meaning Behind This

The BYOA story reflects a broader trend in corporate AI. The first wave of adoption often looked like this: a single large platform is chosen, everything is built within it, and standardization is enforced through restrictions. This works, but it's slow and expensive.

Now, more and more organizations are realizing that there will be many agents – diverse, created by different teams, for different tasks. And what's needed isn't a single monolithic standard, but an infrastructure that can accept heterogeneous agents and provide them with a common operational context.

Simply put: the principle is not «all agents must be the same», but «any agent must be able to work in our environment.»

This sounds logical, but in practice, it requires a certain level of maturity from both the platform and the organization's processes. An agent that can «talk» to the infrastructure through standard interfaces is one thing. An organization that has established processes for reviewing, monitoring, and managing agents is another – and perhaps the more challenging part.

Unresolved Questions and Future Outlook

Open Questions

The BYOA approach looks attractive, but it has its own open questions – and it would be dishonest not to mention them.

First, the standards for agent-infrastructure interaction are still emerging. What is considered an «open interface» today might turn out to be a de facto tie-in to a specific vendor tomorrow. This is normal for a developing market, but it's worth keeping in mind.

Second, operational maturity is not just about tools. Even if a platform can accept any agent, organizations need people and processes that understand how to review, approve, and support them. This is a cultural and organizational issue that technology alone cannot solve.

Third, the OpenClaw example is a demonstration scenario, while real agents in production systems are more complex: they have more dependencies, non-standard data requirements, and specific operational patterns. How smoothly BYOA works in such cases remains to be seen in practice.

Nevertheless, the direction of thought itself seems correct: don't force agents to adapt to the platform, but build a platform that can work with different agents. This is perhaps the only way to avoid drowning in the «zoo» of corporate AI tools that is already beginning to form.