The three stages of an A2A economy
Over the past few years, AI—specifically OpenAI—has transformed what was once an interesting toy into a force that’s reshaping industries and jobs in a non-linear way. Enter the new economic paradigm, known as the agent-to-agent (A2A) economy, where AI agents from different organizations—or even between businesses and consumers—collaborate, negotiate, and transact autonomously on behalf of humans.
At the center of this shift is the fact that intelligent agents, armed with a set of goals and resources, can handle the entire lifecycle of transactions with higher velocity and at greater volume than humans ever will. But, 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.
In this article, we’ll explore these stages to understand how we might soon arrive at a fully autonomous A2A economy.
Stage 1: Functional displacement
We’re already seeing AI agents replace the roles once filled by human specialists, especially since the innovation of vertical AI. This is Stage 1 of the A2A economy, or what we call functional displacement. In this phase, AI agents replace a specific role or function in an organization
For example, think about how chatbots have transformed basic customer support. You arrive at a website and an automated AI agent appears, ready to answer frequently asked questions or guide you through common tasks such as resetting your password or processing a return. Some companies even deploy more specialized AI-driven sales assistants that can respond to queries about product availability or features, and push promotional offers based on real-time customer behavior.
These AI agents offer real business benefits, reducing human workload and cost, improving consistency and speed, while still operating within the boundaries of a specific function, department, and capacity. While AI agents replace certain human roles at this stage, they don’t yet fully replicate or represent entire business operations, since they function best within the confines of well-defined rules, systems, and datasets.
Stage 2: Line-of-business displacement
Stage 2 is more disruptive. At this point, AI agents shift from handling basic functions and tasks to replicating an entire store or business unit.
Consider a retail environment where an AI agent not only answers your questions, but also completes your transaction, arranges shipping, handles returns, provides post-purchase support—all without human intervention—and all in alignment with your preferences and budget.
This is the stage where A2B (agent-to-business) and A2C (agent-to-consumer) transactions become feasible. In such a context, a single AI agent effectively becomes the face of the company, capable of handling a range of services that once required multiple people or departments. It’s a huge leap in efficiency, cost savings, and customer experience.
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 instead interact with an AI agent through a tablet with a voice interface, which completely replaces this entire process. (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 automated 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 AI agent can handle the unexpected, such as edge cases, policy changes, or non-routine issues. That’s why companies leaning in will do so gradually, starting with a subset of products or services before going all in. Once settled in ahead of their competitors, a business would enjoy interesting PR opportunities as well as real business value from cost savings and efficiencies. This stage would initially be met with a mix of customer enthusiasm and potentially some disdain, due to sentiment against AI replacing certain jobs.
Stage 3: AI agent-to-agent transactions
The final stage—AI agent-to-agent transactions—takes the concept to its logical conclusion. Here, businesses and consumers alike set goals and budgets for their respective AI agents, allowing them to negotiate and transact directly with each other. No more browsing websites, learning UIs, or waiting for a human representative; your personal AI agent sources, negotiates, and transacts on your behalf with the AI agent of your chosen business. Your agent secures the best deals, arranges shipping, handles follow-up, and alerts you of any changes to the order status.
On the business side, an AI agent could manage the inventory purchasing schedules, optimize supply chain logistics, or even dynamically set pricing based on the outcomes of 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 in transactions, 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. For instance, OpenAI’s Operator 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 vertical AI agents ready to take orders and interact with other AI tools to streamline transactions and data exchanges. Here, it's time to 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 future: the rise of startups specializing in A2A
As more companies adopt the vision of an economy that runs on AI agents, 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 AI agent era is coming fast to a business near you.
To learn more about the fast-approaching A2A economy, you can check out these related resources:
