If you have been anywhere near the enterprise AI conversation this year, you have heard some version of this. Models are getting good enough that you connect an agent to your systems, give it access through something like MCP, and let it work. The platform layer underneath starts to look redundant. Why pay for infrastructure when the model can reach into your tools and figure things out for itself?

I understand the appeal. I have spent twenty years building businesses on enterprise platforms, and I have watched a lot of expensive software get displaced by something simpler. But that misses context on what really matters. Connecting an agent to your systems is something almost anyone can do now. What decides whether it works, or delivers any value, is far more complex.

You do not have to take my word for it. Over a few days this May, Anthropic and OpenAI each stood up a dedicated enterprise unit to go and do this work inside companies. OpenAI’s launched with more than $4 billion in committed capital, built around forward deployed engineers who sit inside the customer and wire the models into their systems, processes and governance. Anthropic’s is a $1.5 billion venture with Blackstone, Goldman Sachs and others, aimed at mid-sized companies that want AI in production without building a large team to do it.

Two of the biggest model companies in the world just put billions of dollars into this. They built the models, and they still concluded that the model alone does not make AI work inside a real company.

Access is not the bottleneck

APIs have been around for decades. SAP, Salesforce, and your billing system have all had them for years. MCP is a cleaner, model-friendly protocol for exposing those kinds of tools and data sources to agents, and it is useful, but it does not make system integration a new capability. If wiring software together solved the problem, we would have solved it years ago.

An agent can reach into all of those systems and still not know what it is looking at.

Giving an agent access to your systems is like handing someone the keys to a warehouse where nothing is labelled, there is no list of what is on the shelves, and no one has told them which boxes they are allowed to open. They can get in, and if they look long enough, they will find things. They will also get a fair amount wrong, and the things they get wrong change from one visit to the next.

A connection gives the agent reach, and nothing more. It has to work the meaning out for itself, and that is fine until a real decision depends on the answer. The reason it gets things wrong is not the connection. It is what the connection leaves out.

What the connectors leave behind

An agent reaching inside a real enterprise runs into three problems, and a connection solves none of them.

First, meaning. Take one client: SAP calls it a customer, Salesforce an account, the billing system a payer. Someone who has worked there a year knows those are the same company, and the handful of places where they are not. An agent reading from all three has no way to know that on its own. It infers the connections, and it can infer them wrong.

Second, memory, or the lack of it. An agent wired up through a set of tools usually reads the data fresh when it is called, and carries little of your business over between requests. It might read things one way on Monday and slightly differently on Wednesday, and the drift can go unnoticed on both sides. It can come across as inconsistent, even forgetful, starting from scratch more often than you might expect.

Third, action. Reading data is one thing, but acting on it is where a mistake does real damage. An action has to land correctly across the systems it touches, with the right sign-off, a record of who did what, and a way to reverse it. A bank cannot run on an agent that happened to get the permissions right this once. Neither can a hospital, a government department, or a manufacturer with a regulator watching.

None of this gets fixed by a better connector. It gets fixed by a layer that holds the meaning of your business in one place, consistently, with the rules attached. Palantir calls that layer the ontology. The name matters less than the idea behind it. It is the difference between an agent that can reach your data and an agent that understands your business.

In practice it stacks up in layers. Your data becomes objects, the things your business runs on. An airline stops looking at rows in a table and starts working with flights, aircraft and crew. The decisions people make get attached to those objects as actions: rerouting a flight, rescheduling a shift. When someone takes one, the change is written back to the systems it came from. Models and agents then work on top of the objects rather than the raw tables. They reason over the real thing instead of guessing at what a column means. Permissions ride along at the object level, so if a person cannot see a salary field, an agent working for them cannot see it either.

A different category of software

For twenty years, enterprise software asked you to fit your business into its shape. Salesforce had a model of how sales works. ServiceNow had a model of how IT operations run. SAP had a model of how a company handles its money and its supply chain. You bought the model and you bent your business to fit it. That was not laziness on anyone’s part. Building software from scratch was slow and expensive, so a ready-made shape that was roughly right was a sensible trade. Most of the time it fit well enough, and the gap was worth living with.

AI changes the maths on that trade. The cost of building software to fit your business is falling fast. The cost of forcing your business into someone else’s shape has not moved at all. There is also a new reason the generic shape works against you. When an agent reasons over a model that does not quite match how you operate, it inherits the places that model is wrong, and repeats those errors across the work it touches.

So, the platform that matters now holds the real shape of your business and gives your agents a consistent, governed picture to work from. It is a different category of software, and people keep mistaking it for a better Salesforce. The two are not competing for the same job.

The rest of the industry is arriving at the same place, and quickly. The forward deployed engineer that OpenAI and Anthropic built their ventures around is one Palantir pioneered years ago. It sent its own engineers inside institutions to make the software work. But the engineers are only part of the answer. What lets that hands-on work hold up is the platform underneath it, and that is what the startups now pitching themselves as Palantir for X tend to miss. As Marc Andrusko of a16z put it in The Palantirization of Everything, “Palantir works because there is a real platform underneath the bespoke work.”

At Build this June, Microsoft launched Fabric IQ, a shared semantic layer that grounds agents in governed business objects and relationships. It is even calling that layer an ontology. Microsoft reaching for that word tells you the idea has gone mainstream. For Microsoft, though, this is day one. Palantir’s ontology has been running in production for years, across government and industry. It was never an announcement for them. It is how the platform already works.

You can see what that looks like in production. At AIPCon 10, the USDA showed how Palantir’s ontology now underpins national food supply security. Their warning about the alternative was blunt:

“Pointing an LLM at hundreds of disconnected, ungoverned databases gets you a system that hallucinates, is insecure, and unauditable.”

USDA Chief Information Officer, Sam Berry, AIPCon 10

When a platform is overkill

I would be making the very mistake I am warning about if I told you this was the answer for everyone.

If your problem sits inside one function, and the specialist software for that function already solves it, buy the specialist software. If you need a better CRM, go and get one. The ready-made shape exists because it fits a great many businesses well, and rebuilding it for a problem that is already solved is a waste of money. Commodity work stays commodity work, and that is fine.

A platform like Palantir’s earns its place on the other kind of problem: one that runs across systems, where a decision needs your finance data, your operational data, and what is happening in the field at once, and needs them to agree. These are the problems where the way you operate is itself a source of advantage, and where getting the decision right is worth enough to justify building for it properly. Force a problem like that into a generic box and the advantage is gone. You need both

The model itself is turning into a commodity, and fast. Lindy, one of the better-known AI agent companies, recently switched its traffic to an open weight model. The move cut their costs by millions, and performance went up rather than down. For all but the most complex edge cases, you can swap the model out and barely feel it.

There is a tell in how the companies building the models run their own AI. On Lenny’s Podcast, Dan Shipper, CEO of Every, described how one of the big AI labs handles data questions inside the company. Rather than hand their people an agent with raw access to the warehouse, they built a single agent wired into it, one that knows who each person is and what they are allowed to see, with a team whose job is keeping it accurate. They do not trust raw access on its own. They put a governed layer in front of it.

The model supplies the reasoning. What it reasons over still has to be accurate, and that is what the platform holds: a working picture of how your business fits together. On its own, the model is a very capable guess. Give it that picture, and the guessing stops. The companies that get real value out of AI over the next few years will put the two together. The layer underneath the agent only gets more important from here.

Uriah Jacobs is co-founder and CEO of Vanyar, a Palantir Foundry and AIP specialist. Vanyar helps organisations connect fragmented data, build AI agents, and automate decisions. vanyar.com

I’ve spent more than 20 years building enterprise tech businesses. Three were acquired. Each followed a similar pattern.

A platform came along that genuinely changed how organisations work. Demand outstripped supply and the firms that went deep on it, fast, did well.

I built on Salesforce at Cloud Sherpas. I built on ServiceNow at Cloud Sherpas and Thirdera. Now Palantir at Vanyar.

This is the third platform I’ve put my conviction behind. I’m more confident in this one than the other two combined.

A conviction that took a decade to act on

I’ve been watching Palantir for more than 10 years, well before they listed, when most people in enterprise tech still thought of them as a quiet government contractor.

In 2017 I bought into the company in the private markets. Wall Street was beating them up at the time. The complaint was that they were a services business dressed up as software, and that they’d never scale. I disagreed. I’d heard enough from people working with their technology to know they were building something genuinely different.

That conviction sat dormant through Cloud Sherpas, then Thirdera. The timing didn’t line up. I kept watching. After Thirdera was acquired, the obvious move was investing and advising. Lower risk and more time at home.

Then ChatGPT happened, and every CIO suddenly had the same question: how do we actually use this? AIP launched a few months later and the commercial flywheel accelerated. With Thirdera behind me, Palantir’s moment and mine finally lined up.

What’s actually different

Organisations I talk to are wrestling with a version of the same problem. They’ve spent a decade buying SaaS, and now the picture of the business sits scattered across dozens of apps. Each app holds a slice, none holds the whole. Every app brought its own AI, and each one is faster at guessing inside its own slice.

On Palantir, the ontology comes first. The ontology is the model of your business, customers, products, suppliers, employees, orders, shipments, and the relationships between them, captured in a way the platform can work with consistently. It sits between your raw data and everything you build on top, which means once you’ve defined what a customer is, every application, every AI agent, every analyst is working from the same definition.

Most data infrastructure focuses on cleaning and storing data. The ontology focuses on meaning. It captures not just what a customer is, but how a customer connects to orders, contracts, support tickets, and the people who manage them. It captures which actions are valid, who’s allowed to take them, and what other parts of the business get affected when something changes. That’s the layer that’s been missing from enterprise data and the layer everything else, including AI agents, depends on.

Data feeds the ontology. The applications and AI work on top of it.

AI agents that do real work

Building an AI agent is easy. Running one that does real work, without breaking things, is where most enterprises get stuck. How do you let an agent take an action that matters, without losing control of what it sees, what it does, and what happens if it gets something wrong?

An agent that reroutes shipments when a port closes is useful. An agent that reroutes shipments and can’t show its work is a problem. The platform must give you the action and the accountability in the same place. This is what Palantir is built for.

An HR agent built on the ontology sees employee data because the ontology says it can. A supply chain agent on the same platform doesn’t, even though it’s running alongside it. A finance agent might see the GL but not what individual people earn. The permissions don’t live in the agent. They live in the ontology the agent runs on, and every action, every read, every write is logged against that model.

The same platform and the same agents, but with different access. All controlled by the ontology that runs the rest of the business.

There’s a second thing the ontology does that doesn’t get talked about enough. Models keep changing. GPT-5, Claude 5, Gemini 3, whatever the next generation brings. If you build your agents around a specific model, you’re rebuilding every 18 months when a better one ships. Palantir’s ontology sits above the model layer. Swap the model and the ontology underneath stays the same.

Your operating system

Agents are one application of the platform, but Foundry is something bigger. For 20 years the SaaS pitch was: configure our product to fit your business and we’ll run it for you. You bought 80% of someone else’s opinion and customised the last 20%.

That works when the product captures something generic, like email or customer contacts or expense reports. It’s a harder fit when the thing you’re trying to capture is your competitive advantage. Your supply chain logic, your underwriting model, your manufacturing playbook, or the unique way you segment your customers. The stuff that makes you different from your competitors is the stuff that has to be shaped around your specific business.

Your competitive edge is often the way you actually run, the pricing logic, the routing rules, the calls your best people make on instinct. On Palantir, those decisions can move from being tribal knowledge to being part of the system.

What we’re building

Vanyar exists to bridge the gap between Palantir’s commercial momentum and the enterprises ready to use it.

Rahul and I have been around enterprise platforms for two decades. We’ve spent years inside the Palantir ecosystem before starting this one, running development environments, working through the certification track, and on the ground with Palantir’s field team.

We’re excited about what we’re building, and about the conversations we get to have along the way. If you’re thinking about Palantir, or just curious where it might fit in your business, we’d love to hear from you.

What a time to be alive,

Uriah

About the author Uriah Jacobs is the CEO and Co-Founder of Vanyar, a Palantir Foundry and AIP specialist. He has spent 20 years building enterprise technology businesses across APAC and the Middle East, with three acquisitions. He also advises early-stage founders on raising capital and scaling.