The next billion software users will be agentic systems.

General Agentic is bringing enterprise software to the next billion users, enabled by our vertically integrated software supply chain.

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Purpose

1 billion

global activeenterprise agentsby 2030

40x morethan in 20251

of workgets doneby agents

that cannot accessthe software2

18%

of existingenterprise APIspublicly accessible

locking outagentic users3

Vision

Most software usage will arrive through agentic systems.

Not humans clicking buttons on screens.

Most acquirers see AI as a cost story. They run what they buy with less, often rebuilding working systems from the ground up, a gamble their customers cannot afford.

We see a growth story. We unlock software already in production for its next generation of users, repackaged for agents, without disrupting the pathways today’s users rely on. We believe this opens a far larger market at a minimal added cost.

Systems

One system, from the network layer through the business logic.

A reference implementation of the complete software supply chain, built and operated by General Agentic. Multi-tenant, cloud-agnostic, and model-independent from the ground up.

Network

Our own network, compute and data fabrics lead to fixed-cost compute. Owning the root of the stack keeps the economics predictable as agentic usage grows.

Supply chain

The machinery that moves software to its new users: continuous integration with real-time delivery, identity, connection APIs, permissioning and governance, and an SDK for seamless handoff. One integration at the core, not one per platform.

Business logic

The validated logic of the software we acquire, served to agentic systems at full fidelity. We do not rebuild it, we deliver it.

Our founders run the firm on this system, including the application that built this very page. We were its first users and the companies we partner with are its next.

Partnership

We partner with companies through acquisition.

Our process

We acquire established software companies and take majority control. Your name stays on the door, your customers keep the product they rely on, and when their agents arrive, it is still your program powering their work.

Your customers bring their agents, you bring the logic they trust, and General Agentic brings the technology that connects them.

In this era, software splits into a rewrite or a repackage. We buy where the natural path is a repackage. We keep the system of record, we do not rebuild the core, and we connect your product, as it exists, to every agentic workspace your customers use.

Phase 1 (First and Second Quarters)

Connect identity, leave the product alone

Nothing changes for your current customers. We connect your identity system and APIs to a governed distribution layer, and together we configure permissioning so an agent can work at the same fidelity a human user can. You control what is exposed, and to whom. People keep the sign-in they already have.

Phase 2 (Third and Fourth Quarters)

Open a governed route into the workspaces they already use

Your product becomes reachable from ChatGPT, Claude, and the other surfaces where work is starting. To users, it's the same product, under the same entitlements, just delivered through a new channel. Usage starts to climb, and we can test pricing models that are a win-win with the customers rather than only seats.

Phase 3 (Second Year Onward)

Ship the roadmap through that route

Development continues as you planned. Every new feature can reach those workspaces without a second architecture. Your team inherits the tooling to do that at product velocity. Your customers bring their own agents and pay their own inference. The roadmap starts growing where the new users are.

The results show up where it counts: new users on the same product, more revenue per customer, and a channel you can price, because the work now starts where your customers already work.

How we help

Growth is our focus, efficiency is our engine. We study the frontier of Silicon Valley and Wall Street, prove it out on ourselves, and install what works in the companies we own. We relentlessly source and test tactics and technologies that help our companies grow faster, more efficiently.

Growth

Build

Product Development & Velocity

We help you build for agents, faster. This requires new surfaces, new permissions, and new features designed for agentic users. Your team starts from our working system, not a blank page.

Sell

Sales, GTM & Customer Success

New users deserve new pricing. Agents don’t buy seats, so we help you price what the product actually delivers: usage, tiers, and outcomes without breaking contracts. Buyers increasingly choose software their agents can reliably use, and every agent they deploy expands the account while tying your product deeper into their work.

Efficiency

The stack

Software Supply Chain

Success in the agentic channel multiplies usage, and metered infrastructure bills you for it. Over time the supply chain centralizes onto the one system we operate, the way a well-run fleet is renewed: opportunistically, piece by piece, as each part reaches the end of its useful life.

The business

Finance, Accounting & Back Office

Every company carries work that has nothing to do with its product. Finance, accounting, payroll, and reporting benefits as our portfolio scales. Your team’s time goes back to making the best products for your customers.

What we don’t change

You spent years earning your brand and your customers’ trust. That is the asset we are buying. We keep the core product, the name on the door, and the management that built it. Although we take majority control, we do not hire a new general manager and our structure allows additional partners to participate as the platform scales. Our technology and our capital exist to compound what you built through the next platform shift.

Company

General Agentic was founded on a straightforward principle:The best acquirers of technology companies are builders of technology themselves. The firm pairs Silicon Valley technical discipline with Wall Street investment judgment, and operates from New York.

The firm’s founders bring experience from world-leading institutions.

Thoughts

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Thought PieceRead

Agent in the Loop

Agents are a form of labor and software programs are a form of capital. Labor uses capital. In software, that labor shows up as a user.

September 2026 · 5 min read

Jake Chasan, Zach Evans

Thought PieceRead

The Missing Metric

The market today has not decided what the “miles-per-gallon” should be for the AI era. That is why pricing remains so opaque and difficult to understand.

September 2026 · 8 min read

Jake Chasan, Zach Evans

Thought PieceRead

The General Manager's Folly

The decision maker who can see whether an asset’s natural path is a rewrite or a repackage will win this era. In every technological wave, from the iron horse railroads to the agentic software applications, the same question is posed, and the same result rings true.

August 2026 · 4 min read

Jake Chasan

Founder LetterRead

A Letter to Our Future Partners

You spent years turning hard-won judgment into software. The industry calls that a feature set. We think it is something more valuable. It is business logic, the encoded knowledge of how work gets done in your vertical, and it is the most durable asset in the developing AI Economy.

August 2026 · 6 min read

Jake Chasan, Zach Evans

01 / 04

Thought Piece5 min read

Agent in the Loop

Agents are a form of labor and software programs are a form of capital

By and · September 2026 · New York, NY

The concept of human-in-the-loop is well understood. As machines become more capable, they take on more tasks. The downside risk of each action compounds, and the rule of thumb is to place a human as the safety stop, like a circuit breaker, before the tipping point.

Agent-in-the-loop is complementary, not a replacement: a specialized agent serving as a safety stop for other agents, so a hierarchy can take on more responsibility than one system can hold.

Human-in-the-loop is where the enterprise sits today. Agent-in-the-loop is how the loop scales once the principal, the identity, and the tools already exist.

To understand why, one can look to the classic macroeconomic models from Solow, Romer, and Jones. Labor (L) is more flexible than Capital (K), but it is less efficient at repetitive tasks. In the classic sense: labor is the human and capital is the tool. The human doesn’t need the tool, as many humans given a long enough time horizon can get to the outcome; however, the tool needs the human or else it will sit idle. Add in a delivery timeline or required repeatability, and the equation becomes profitable for the human to buy or build the tool.

Wall Street and Sand Hill Road are missing the point:

An agent is a form of labor and a software program is a form of capital.

Labor uses capital. In software, that labor shows up as a user. The same way a human can in theory get to an outcome without a tool, an agent can in theory arrive at an answer without the existing software program. That is the long path, fraught with hidden landmines that the tool safely mitigates, not the default. Software needs the agent or it sits idle on the hard drive. Given a deadline or a requirement to run the workflow repeatedly, it becomes profitable to invest in the software that already holds the work, and to make the agent a full user of it.

With the understanding that agents are a form of labor and software is a form of capital, the next area of value comes from the two forms of specialization: nature and nurture.

For humans, nature is the genetic endowments given at birth whereas nurture is the impact of one’s surroundings.

For agents, the same concepts apply one-to-one. Agentic nature is the endowments of code and packages the systematic being has access to. Agentic nurture is the systems it lives inside: identity, files, mail, the time-tested software that already runs the enterprise. Humans can undergo surgery, agents can change the hardware that runs them. Humans can take medication to change their state, agents can change the packages that run their harness. Humans can change the rooms they work in, agents can be placed in different systems. Nurture is why distribution to existing software matters more than a new model. It is what makes the labor productive on top of the capital.

The term agent isn’t clearly defined. An agent executes under the direction of a principal. In economics the same person is often both, in different rooms. A Vice President at Goldman Sachs is the principal when working with an Analyst, who is the agent in that room; the Analyst is the principal when working with Microsoft Excel. The purpose of a Vice President at Goldman Sachs is precisely to be the agent of the Managing Director (who is the agent of the client) and the principal to the Analyst. They are different versions of the same job, with different requirements as to their context windows. The Managing Director needs to know about the company’s strategy and the range of possible outcomes. The Analyst needs to understand every penny flowing through the business to generate the materials from the most granular details on up.

The Vice President has to translate the context from the Managing Director (a mile wide and inch deep) to the context of an Analyst (a mile deep and an inch wide) and check that nothing is lost in translation, which in the AI world would be known as a hallucination. In a working system those roles stay named. The principal is a user who owns the work. The agent is the runtime that executes. The software is the capital. Collapse the three and there is no one to proceed, no one to catch the translation, and no reason to keep the system of record.

The separator between the Analyst, Vice President, and Managing Director is experience, not any inherent change of composition. The same will play out with AI agents.

As the systems become more capable, there will be levels of AI agent employees the way there are levels of human employees, from individual contributor to manager.

Because of this, there will be a new software layer required that lets the enterprise structure run: identity, governed tools, and a specialized agent that evaluates another agent’s work before anything irreversible happens.

This is why it remains challenging to value AI in the enterprise today: companies are trying to evaluate and price AI before it has the required tools and software to execute its work reliably. Value compounds when the loop is repeatable, not when each agent is asked to reinvent the wheel. This will be shown in the coming years as the capital that labor uses is a new form of the time-tested software that the enterprises already run on.

Jake Chasan & Zach Evans

Co-Founders of General Agentic