Building Reliable Enterprise AI: Why the Model Is Only Half the Battle

Want the full technical picture? Our CEO Eric has written a detailed engineering deep dive on this topic. Read the original post on his Substack →

There's a lot of excitement right now about AI agents: software that doesn't just answer questions, but takes action, researching, drafting, analyzing, and completing complex multi-step tasks on your behalf.

Most of that excitement is focused on the AI model itself. Which model is the smartest? Which one reasons best?

At Vertesia, we think about something equally important, and far less talked about: the infrastructure that makes AI agents work in real-world business environments.

What is an AI agent harness? (and why your infrastructure needs one)

Think of AI agents like F1, if you will. The driver (the AI model) gets all the attention: the skill, the speed, the decision-making. But without a reliable car, a pit crew, a race track, safety systems, and a communications team, that driver can't race at all. They also would be in a totally unsafe position.

The harness is everything around the AI model that makes it safe, reliable, and effective. It's the layer of technology that:

• Remembers what the agent was doing, even if a server restarts or something goes wrong mid-task
• Keeps your data and credentials secure, so the AI can take action on your behalf without ever directly "seeing" your passwords or API keys
• Handles many users at once, so your AI platform serves your whole organization, not just one person at a time
• Keeps a full audit trail, so you always know exactly what the agent did and why
• Lets tasks run for hours or days, so an agent can start a job on Monday and finish it on Wednesday, even if no one is watching

For developers, who build tools for themselves, a simpler setup for building one off agents works fine. They're the only user, and if something crashes, they just restart it. But for a business deploying AI to hundreds of employees across dozens of systems? That simpler approach breaks down fast.

Scaling enterprise AI: how Vertesia builds a reliable runtime

To build this kind of reliable, enterprise-ready harness, Vertesia uses a technology called Temporal.

In technical terms, Temporal is a durable execution platform that ensures complex, long-running workflows continue correctly even if individual servers crash, networks fail, or code gets updated. It's the reliability backbone that enterprise systems need.

Temporal is excellent at what it does. But on its own, it doesn't know anything about AI agents. It doesn't understand AI models, tools, conversations, or credentials. That's where Vertesia comes in.

Merging the AI harness with Temporal: the Vertesia advantage

Vertesia took Temporal's reliability foundation and built a complete AI agent runtime on top of it. That means we added all the pieces that turn raw infrastructure into something an enterprise AI agent needs:

• A smart tool system: agents can discover and use the right tools for each task, with permissions enforced at every step
• Secure credential handling: sensitive information is decrypted only for the instant it's needed, then discarded
• Real-time streaming: users see what the agent is doing as it works, not just a result at the end
• Multi-user isolation: hundreds of users can run agents simultaneously with complete separation between their work
• Full audit logging: every action, every tool call, and every decision is recorded and queryable

The AI model, whichever one powers the agent, sits on top of all of this. And because the harness is separate from the model, we can adopt new and better models as they emerge without rebuilding the platform underneath.

The bottom line: why AI infrastructure matters for your business

The most sophisticated AI model in the world is only as reliable as the infrastructure supporting it. For enterprise use, where agents work across teams, handle sensitive data, run unattended, and need to be accountable, the harness is the product.

That's the platform Vertesia has built: one designed from the ground up for the demands of real business environments, not just developer experiments.

Curious about the technical details? Our CEO Eric’s original post goes deep on the engineering decisions behind Vertesia's distributed agent architecture. It's a great read for engineers and architects evaluating enterprise AI platforms. Read the full technical deep dive →