In just a few years, AI—specifically OpenAI—has transformed what was once an interesting toy into a force that’s reshaping various industries and jobs in a non-linear way. Enter the new economic paradigm, known as the agent-to-agent (A2A) economy, where AI-driven agents from different organizations—or even between businesses and consumers—collaborate, negotiate, and transact autonomously.

At the center of this shift is the idea that an intelligent agent, armed with a set of goals and resources, can handle the entire lifecycle of transactions at a much higher velocity and volume than humans ever will. Yet, like any good disruptive innovation, it doesn’t arrive all at once. Instead, it unfolds in stages, each with distinct levels of complexity and adoption. Below, we’ll explore these stages and look at how we might arrive at a fully autonomous A2A economy.

Stage 1: Functional Displacement

We’re seeing agents replace specialized roles once carried out by specialists. Think about how chatbots have transformed basic customer support: you enter a website, and an automated agent appears, ready to answer frequently asked questions or guide you through common tasks such as resetting your password or arranging an exchange for your purchase. This is Stage 1, or what we call functional displacement. In this phase, AI agents replace a specific role or function in an organization—such as answering common user inquiries, processing returns, or handling initial triage in support tickets. Some companies even deploy AI-driven sales assistants that can respond to queries about product availability, features, and push promotional offers. These agents reduce human workload and cost, improving consistency and speed. Yet they still operate within the boundaries of a specific function, department, and capacity. They function best within the confines of well-defined rules, systems, and data sets. While they replace certain human roles, they don’t fully replicate or represent entire business operations.

Stage 2: Line-of-Business Displacement

Stage 2 is more disruptive, as it goes beyond handling mere functions or tasks. Here, the agent can replicate an entire store or business unit. Imagine a retail environment where an AI agent not only answers your questions but can also complete a transaction, arrange for shipping, handle returns, and continue to provide support after the sale. This is the point where A2B (agent-to-business) and A2C (agent-to-consumer) transactions become feasible. In such a setup, a single agent effectively becomes the face of the company, empowered to handle a range of services that once required multiple departments or people. It’s a huge leap in efficiency, streamlining customer interactions.

Imagine being able to walk into a Blue Bottle store, but instead of talking to someone taking an order—“What can I get you today? … What size? … Would you like oat milk with that? … Can I get you anything else? … For here or to go? … And your name for the order? … That’ll be $5.99 … Thank you!”—you interact with an AI agent on a tablet that completely replaces this process through a voice interface. (A nice accessibility bonus is that this AI agent will be able to speak in almost any language to the customer—even sign language using a virtual avatar that appears on the tablet.)

This kind of experience—offline or online—also demands robust integrations with payment systems, order placement, inventory management, shipping, and CRM platforms. The real challenge lies in ensuring the agent can handle the unexpected: edge cases, policy changes, or non-routine issues. That’s why companies leaning in at this stage often do so gradually, starting with a subset of products or services before going all in. Once they are settled in ahead of their competitors, it will not only give them some interesting PR opportunities but also real business impact—real cost savings—initially met with a mix of customer evangelism and potentially some customer disdain due to sentiment against AI replacing certain jobs.

Stage 3: Agent-to-Agent Transactions

The final stage—agent-to-agent transactions—takes the concept to its logical conclusion. Here, businesses and consumers alike assign budgets and set goals for their respective AI agents, allowing them to negotiate and transact directly with each other. No more browsing websites, learning UIs, figuring out how to do certain things on a new app, or waiting in queue for a human representative; your personal AI sources and negotiates the best deals, arranges shipping, handles any necessary follow-up, and alerts you when there are any noteworthy changes to the order status.

On the business side, an agent might decide on inventory purchasing schedules, optimize logistics, or even dynamically set pricing based on the outcomes of these agent-to-agent negotiations. It’s an economy driven by AI, with human oversight and approvals focusing on broader strategic objectives. This stage promises unprecedented efficiency and speed, but it also raises questions about trust, accountability, and the ever-present concern of job displacement.

Transitions: OpenAI’s Operator and the Hybrid Model

Before we reach Stage 3, some transitional models are emerging. OpenAI’s Operator, for instance, is a good example of the transient hybrid approach. An AI clicking through websites that load beautiful CSS and UI elements with rounded-cornered buttons with drop shadows is probably not the most efficient use of traffic or resources for both sides. Soon, businesses will have AI agents ready to take orders and interact with other AIs to be more efficient in their transactions and exchange of information. Cut out the “human element” to optimize for agent-to-agent interactions.

In many B2C interactions today, customers remain human while the business side deploys AI agents. That’s the B2A (business-to-agent) arrangement. But the next logical step is to equip consumers with their own AI counterparts, bridging the gap until A2C becomes mainstream. While it may seem inefficient to keep partial human involvement, these hybrid models allow companies to build trust, develop and refine agent capabilities, and work out kinks in the system before everything goes fully autonomous.

The Rise of Startups Specializing in A2A

As more companies adopt this vision, a new breed of startups will emerge rapidly. These startups will deploy turnkey AI agents that can represent an entire business—handling marketing, sales, customer support, payment, and fulfillment. Their pitch is simple: “Give us your brand guidelines, product catalog, and business policies, and we’ll deploy an AI-run storefront,” or even more simply, “Give us access to your Shopify account, and we’ll deploy an AI agent that represents your entire store experience.” Over time, these agents will increasingly interact more with other agents than humans, evolving into a self-sufficient ecosystem of A2A transactions.

The implications are huge: imagine supply chain negotiations, wholesale orders, or even consumer shopping experiences happening almost entirely between AI systems. Humans will still provide the guardrails—setting budgets, policies, and goals—but the day-to-day operations might run on AI autopilot.

The agent era is coming fast to a business near you.