The agent platform that treats every integration as a user

About Authenti.ca

Authenti.ca was built by operators who had run the playbook by hand. Forward Deployed Engineers, the role Palantir popularized, sit inside a customer’s operation, learn the workflow, and ship automation against it. The new product turns that role into software: AI Forward Deployed Engineers that embed inside enterprise supply chain teams, learn the business from documents and conversations, and act across the systems the team already uses.

The company calls itself “Palantir for the rest of us.” Its agentic AI platform is built around AI FDEs that audit trade invoices, plan sales and order flow, and act as ongoing operational headcount for customers who would never sign a ten-million-dollar Palantir contract.

The integration challenge

That product premise has one architectural consequence: the agents have to read and write across each customer’s ERP, CRM, inventory, purchase orders, sales orders, and email. Integrations are not a feature for Authenti.ca. They are the foundation. Without them, the agents have nothing to act on.

“The bottleneck is not building,” said Michael Borg, CEO and co-founder of Authenti.ca. “It’s going through the bureaucracy and the security necessities.”

Results

Borg, an AI-native operator, picked Paragon on day one and never wrote a hand-rolled integration platform himself. Five months from launch, Authenti.ca’s agents are in production at customers across defense, manufacturing, industrials, and food. Retail is next.

The challenge: Integrations are the gating factor for every enterprise agent

Most agentic AI companies hit the same wall. The agents look great in a demo. Then a customer asks how the agent will read their NetSuite, write to their Zoho, pull invoices from their QuickBooks instance, and authenticate against systems each end user has different access to. Without a clean answer, the deal stalls.

“Integrations are likely one of the biggest bottlenecks you’ll face in providing ROI to the customer,” Borg said. “The more you can connect your agents to the edges of their domain or even across the rest of the organization, the better the solution you can provide.”

That meant integrations could not be a product feature Authenti.ca built out over time. They had to work on day one of every customer engagement, hold up at enterprise scale, and stop consuming engineering time the team needed to spend on the agent intelligence layer.

“Without Paragon, our agents would have a much larger surface area of failure modes. That’s the solution we like with a paying customer using this in production. You don’t want to have to worry about that stuff.”

Why an AI-native CEO buys an integration platform for the SDK

Borg evaluated Prismatic, considered building it himself, and chose Paragon. The reason is contrarian to how the integration platform category usually gets sold.

“The thing for me that initially sold me on Paragon was the SDK. I love that you guys have that no-code UI. I don’t really use it.”

“If the integrations as a service provider has a stable SDK, we can leverage codegen AI to build out these integrations much more quickly. That was the thing that got us to use you guys right out the gate.”

Most integration platforms sell on the visual builder. The AI-native buyer wants the opposite. A stable, opinionated SDK is the substrate AI code generation needs to actually produce usable integration code. The no-code UI is less load-bearing when nobody on the team is writing integration code by hand.

“We don’t write code, we just review it these days.”

Michael Borg, CEO and co-founder, Authenti.ca

For an AI-native team, the integration platform’s value is in two layers most categories barely name:

That second column is the buying decision for Authenti.ca. The 45-minute NetSuite story is what it looks like in practice. Partly custom, partly off the shelf, live for an AI FDE the same morning. The hard part of the work was security review, not engineering.

Auth is the silent killer of agentic AI

Borg has a foil for the 45-minute build story. He recently set up a QuickBooks Enterprise Desktop integration for a customer, on-prem, hand-rolled, with no managed sync available.

“It’s a pain in the ass. When you’re doing it yourself, you’re dealing with a lot of stuff. Auth issues, token refreshing. It’s especially annoying when you have to help the customer re-authenticate. That’s a huge blocker that can lose you days or sometimes weeks.”

Auth is the part of integration work that does not show up on the roadmap and quietly burns the engineering calendar anyway. Expiring OAuth flows, refresh tokens, and customer admins who have to re-authorize after a permissions change are not technically hard problems, but each one costs days. When an agent is supposed to be acting inside a customer system, an auth failure does worse than slow Authenti.ca’s team. It stalls the customer’s AI in front of the customer.

The architectural decision that makes the second column possible is worth a full paragraph of detail.

Every Authenti.ca agent is treated as a real user across the customer systems it touches. Each user has their own credentials, scoped to what that user is allowed to do. Token refresh, expiring grants, and credential maintenance live below the line where Authenti.ca’s product engineers think about them. The agent calls the same Paragon-managed action a human user would call, with the same scoped permissions, and Paragon handles the lifecycle around it.

“We treat an agent as a user across all of these systems. That user-level auth maintenance is really helpful.”

Michael Borg, CEO and co-founder, Authenti.ca

This is what makes autonomous agent behavior safe in production. The agent does not bypass the customer’s permission model. It operates inside it.

The architecture: A constrained ontology, agents as users, and Managed Sync as the safety rail

Authenti.ca’s architecture has three load-bearing concepts. Paragon does the work in all three.

1. The ontology. Authenti.ca builds a knowledge graph for every customer organization, a digital twin of the customer’s business. The agents do not freely call tools. They call tools that have been scoped to them by the ontology for a specific task.

2. Agent as user. Every agent is treated as a real user across customer systems. Paragon handles user-level auth, token refresh, and credential lifecycle. The agent operates inside the customer’s permission model.

3. Managed Sync as the safety rail. Agents that explore customer systems live tend to exhaust API limits inside a day. Managed Sync gives them a normalized, rate-friendly view of the data to build the ontology against before they ever take an action.

“The ontology constrains the actions that are called by the agents. In all those cases, it’s some Paragon integration.”

This is the part of the design that earns the enterprise customers. Agents that can do anything are dangerous. Agents that are constrained to a specific catalog of actions, with predictable failure modes, are deployable. Borg’s team runs evals against the workflow agents and shares the results with customers. The known failure category, often around five percent of runs, gets human review. The rest auto-passes.

“Managed Sync allows the agents to just explore. That’s what matters in those early days with a customer, understanding the business and building out the ontology.”

Borg recently maxed out his Managed Sync allocation in his demo environment and asked for more capacity.

The outcome: Walking into sales meetings with a digital twin

Authenti.ca walks into every prospect’s first demo with a digital twin of the prospect’s business already built. The ontology is assembled from publicly available data and prior conversation transcripts, ingested through Paragon connectors. The agents have not seen the customer’s real systems yet, but they have seen enough of the customer’s world to demo against it.

“Demo one includes a custom ontology, a digital twin, for the organization we’re pursuing. All of that is automatically ingested through the Paragon connectors that we have.”

Michael Borg, CEO and co-founder, Authenti.ca

As the deal progresses, more documentation flows in. The ontology updates. The agents get sharper. Once the customer signs, Authenti.ca connects the real systems and the AI FDEs go live.

Security comes up early in these conversations because Authenti.ca’s customers are in defense, manufacturing, and industrials. Borg said it barely comes up.

“From a data processing standpoint, any third party we use has to be SOC 2 Type 2. So it’s a non-issue.”

One Borg anecdote does more work than any feature page on the topic.

“I scraped all of the logos, the integration logos from your website, and I just put it on ours. Almost like a mirror of your integrations page. Being able to show that off the shelf is huge for customers.”

The bottom line: Advice for engineering teams building on agents

If there is one thing Authenti.ca’s experience makes clear, it is that the real cost of integration infrastructure for an agent platform is not the build. It is everything that comes after. Auth lifecycle. Edge cases. Rate-limit handling. The on-call surface area when the customer’s AI stalls because a token expired.

“Without Paragon, our agents would have a much larger surface area of failure modes. That’s the solution we like with a paying customer using this in production. You don’t want to have to worry about that stuff.”

Building on enterprise data? See how Paragon’s Managed Sync handles authentication, schema mapping, and sync infrastructure so your team doesn’t have to.

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