The agentic company: How startups are leading the way

Many companies today operate in a surprisingly analog way. Critical knowledge lives in people's heads, surfaced via emails, meetings and spreadsheets. Important information can be stored in different channels, minimizing opportunities to gain real time insights and context.

Patchworks of processes and habits mean employees become the fulcrum for conveying vital information. This paradigm was a result of a “slow” world, where people were the focal point of interaction between data and business processes, both inside and outside a company — with customers and suppliers. But today, we are starting to see the emergence of a new dynamic – native agentic Al companies that have designed their business models around this new technology.

At the heart of this effort are startups that are leveraging agentic Al as their operating systems. To draw a comparison from the racing world — instead of driving a formula-1 race car in the streets of New York, new startups are building a whole new racetrack, enabling their business to run at 200 mph. These startups are leading the way in showing legacy enterprises how to re-organize for the agentic era.

The future is agentic

While we don't have all the technology available today, we are already seeing agentic-oriented business models challenge legacy ones, with a growing number of companies incorporating an agentic mindset from day one. Al-native startups are designing processes with agents as first-class citizens, not add-ons. Employees are expected to manage agents and deliver faster than ever, enabling their companies to iterate faster than ever on their product and business strategies. In sales, operations, marketing and finance, agentic architectures are already driving costs down, yielding faster innovation cycles and creating entirely new ways of working.

As a result, we are seeing companies scale faster than ever before, not because they hire more people or buy the most furniture, but because they have figured out how to operationalize compounding knowledge.

One critical point: native agentic startups do not want their main selling point to enterprises to be about hiring fewer humans. Instead, it is about fully understanding how organizations work and executing on that intelligence. In other words, companies that succeed will not just use agents, but instead will be agentic.

Startups are also figuring out how to price their agentic offerings, test driving models that can be scaled at an enterprise level. Although no single model has emerged, startups are testing out outcome-based pricing, where customers pay per unit of work completed, consumption-based pricing, similar to foundation model token pricing and seat-based pricing, which is the traditional SaaS pricing model.

The four layers of an agentic company

Legacy enterprises have a lot to learn from startups on this front. Native agentic companies have innovated not only in what they offer product-wise but also in how they operate. Importantly, these startups can show enterprises how to build in layers, not verticals, where software agents progressively codify how work actually gets done.

• The first layer is single-user automation. Here, agents are run and governed by individual users, codifying roles at a functional level. Think of a group of agents that knows exactly how you reconcile accounts, prepare sales follow-ups, or screen candidates — and does it consistently, every time. These agents work with a tight feedback loop and can learn from other agents of employees in similar roles.
• The second layer is inner-department orchestration. Agents within the same department engage with one another to complete tasks end to end. This is where business operations are codified at the department level: finance, sales, marketing, HR, product. Work stops being a sequence of handoffs and becomes a coordinated flow.
• The third layer is cross-department collaboration. Agents operate across departments, driving company-wide business processes. Revenue forecasting, hiring plans, product launches, and customer retention are no longer siloed. This layer codifies how the company actually operates as a whole.
• The final layer is company-wide orchestration. Governing agents oversee both strategy and execution, continuously aligning tactical actions with long-term goals. This is the codification of business strategy itself — not as static slides, but as living, executable logic.

This meeting should have been an agent...

It is easy to see how enterprises could benefit from this new way of startup-inspired agentic thinking.

Imagine an enterprise wants to pivot its existing finance function into an agentic-oriented posture. Bookkeeper agents would continuously record and classify business transactions in real time. Controller agents could aggregate those transactions, validate them, and generate financial statements automatically. FP&A agents would interact with both bookkeeper and controller agents to build rolling forecasts, scenario analyses, and projections. Above them, CFO agents would synthesize inputs from all finance agents to shape financial strategy, capital allocation, and risk management.

But it does not have to stop there. From an agentic perspective, finance agents would interact with sales, marketing, operations and HR agents to monitor performance and project outcomes across an entire business. The result is a system that takes a holistic view of a company holistically and receives continuous updates of that view.

This is not pure fantasy. Startups like Applied Compute are revolutionizing enterprise Al with Reinforcement Learning-as-a-Service (RLaaS). This allows organizations to continuously train and fine-tune Al agents using real operational data, moving beyond static instructions. RLaaS platforms use existing enterprise workflows to create high-quality training signals, leading to agents that are autonomous, resilient and precise, ultimately improving decision quality and automating tasks.

The impact of scaling such change at the enterprise level is profound. Employee productivity could increase dramatically as humans move from execution to oversight and judgment. Meetings could disappear (or at least shorten) because coordination happens agent-to-agent. Decision-making would thus accelerate, become more informed and more adaptive. Ultimately, an agentic-centric company could become more resilient, agile and responsive in days, not quarters.

Conclusion

While generative AI technology is advancing at an astonishing pace, we are still missing critical building blocks to make the agentic-oriented vision a reality at scale. Most importantly, startups need to sell both a shift in product and purpose: we need to rethink what knowledge actually is and how we can capture it effectively.

Humans attach meaning to information based on history and situational awareness. Machines do not, unless we teach them how. Teaching a machine context, not just analysis, will take time. But wondering if this could happen is no longer a consideration — instead we are asking when. As humans partner with Gen AI to create new tools, a cultural shift will take place and in just a few years business models that were once seen as innovative will look antiquated.