For years, the story in AI has focused on raw power. Bigger models. More parameters. Faster chips.

But inside real organizations, the problem your teams feel every day is not a lack of compute. It is the interface.

People still spend hours clicking through portals, filling out forms, searching across different systems, and repeating themselves on every call and channel. The machine has gotten faster, yet the way we work with it has hardly changed.

At Stochastic, we believe the next leap in productivity will come from a new kind of interface, one where machines finally adapt to humans. We call that interface a Thinking agent.

The founding insight: it was never about the chip alone

Our team has spent years building the processors that power how machines see, hear, and understand. Glenn Ko, our founder, led work across Samsung, IBM, Qualcomm, and Harvard on the underlying hardware that enables modern AI.

A pattern kept showing up.

Even as systems got faster and more capable, people were still doing the same work around them. Hunting for the right field. Copying reference numbers from one system to another. Explaining the same issue over voice, email, and chat to different teams.

Every major leap in computing came from a change in interface, not just more compute:

• Command line to graphical interfaces

• Desktop to mobile

• Mobile to always on voice and notifications

We believe the next leap is intelligent interfaces, systems that understand how your specific organization works and that act on your behalf.

The four levels of human machine interface

To make sense of where enterprises are today, it helps to think in levels.

Level 1: Command and workflow tools
This is where most organizations still live. Portals, dashboards, forms, menus, scripts, and search bars. These tools follow fixed rules. They do not reason or plan. People must learn how each system works and adapt to it.

Level 2: Assistants
These are search helpers, FAQ bots, and basic copilots. They can answer questions and make suggestions, sometimes with light reasoning. But they do not take real work off your plate. The human still drives every action.

Level 3: Reasoning agents
These agents can plan a sequence of steps and perform them when you ask. They can, for example, take a task and call a few different tools in a row. However, they do not truly remember you. They do not learn your processes deeply. Each session starts almost from scratch.

Level 4: Thinking agents
This is where we focus. Thinking agents are always present in the places you already work. They remember context over time, understand how your teams handle different situations, and act across systems on your behalf. Over time, they adapt to your language, your policies, and your edge cases.

In short, lower levels force humans to adapt to the system. Thinking agents flip that, so the system adapts to the human.

The hidden cost of bad interfaces

You can feel this in the numbers.

Knowledge workers commonly lose more than an hour a day just navigating systems, searching for information, and coordinating across teams. Frontline staff, like call center agents or clinic coordinators, can juggle five to ten systems during a single interaction.

In healthcare, administrative overhead has grown into hundreds of billions of dollars per year. Much of that is not core care. It is people doing work for the systems rather than systems working for the people.

This is the friction that Thinking agents are designed to remove.

What is a Thinking agent?

A Thinking agent is an AI system that acts like an always available teammate, not a one off chatbot.

At a high level, it has three key abilities:

1. Memory
   It remembers what matters at three levels. The organization, the team, and the individual. This includes your terminology, your policies, your typical resolutions, and the details of ongoing cases or tasks.

2. Reasoning
   It can take a real world request, break it into steps, and decide what to do next. This often means coordinating different tools, handling exceptions, and knowing when to escalate to a human.

3. Guardrails
   It operates within clear boundaries. It respects privacy, follows compliance rules, and has clear conditions where it asks for help instead of guessing.

These abilities are powered by our Agent Computer, the platform that combines memory, reasoning, and guardrails into one controllable system. It connects to the channels you already use, such as phone, chat, SMS, and email, and to the systems where your work happens, such as EMRs, CRMs, ticketing tools, and document stores.

A healthcare example: from more clicks to fewer burdens

Consider a healthcare setting, such as a clinical operations team supporting patients, providers, and staff.

Traditionally, a patient call can involve:

• Pulling up the right record in the EMR

• Checking recent notes and prior authorizations

• Looking at scheduling rules and insurance details

• Updating forms or sending follow up messages

Each step often lives in a different tab or system. The staff member spends much of the call navigating software, not actually helping the patient.

With a Thinking agent in place, the experience is different.

The agent listens, understands the request, and handles the routine steps across systems. It brings the right information into view and takes on the repetitive updates. The staff member can focus on the human parts of the interaction.

One clinical operations leader put it simply:

“I expected automation to make our processes faster. I did not expect it to improve consistency and reduce administrative burden this much.”

Consistency improves because the agent applies the same rules every time. Burden drops because the team is no longer acting as a bridge between systems. The Thinking agent is.

Why we insist on privacy and control

For a Thinking agent to be truly helpful, it must learn from real work. That includes patterns in your tickets, your calls, your documents, and your workflows.

To make this possible and safe, we design Stochastic to be private by default.

• You choose the deployment model, such as your own cloud, VPC, or on premises.

• You control how data flows, how models are updated, and what the agent is allowed to do.

• You keep ownership of the insights, the improvements, and the behavior that the agent learns.

In other words, you are not plugging your business into a generic public model. You are building a Thinking agent that knows your business and that lives inside your environment.

From pilot to partner: how Thinking agents improve over time

The first version of a Thinking agent usually starts with a focused workflow. For example, resolving a specific type of support ticket or handling a common class of patient calls.

From there, it improves in a few ways:

• It learns new variations of the same workflow from real outcomes.

• It picks up your language, from internal abbreviations to product names.

• It adapts to team preferences, such as how to phrase messages or when to escalate.

As it improves, you can safely expand its scope. More workflows, more channels, more autonomy, always under clear guardrails and with human oversight where needed.

Over time, the agent goes from being a tool that helps with a task to a partner that carries entire parts of the process.

Where this is going

We believe every team will eventually have Thinking agents as part of its core stack.

• Operations teams will use them to coordinate work across systems.

• Support and clinical teams will use them to handle routine contacts and surface the right context.

• Knowledge workers will use them to search, summarize, and act across the tools they already use, without jumping between windows.

The technology for this is here. The differentiator is not raw model size. It is how well the interface understands and adapts to the people who use it.

That is why we built Stochastic.

We want to give enterprises a private, controllable, and truly helpful interface, one that finally lets the machine adapt to the human, not the other way around.