Why AI Agents Are Changing the Economy

Key Takeaway

Most discussions about the economic impact of artificial intelligence begin with productivity and jobs. How much time will AI save? Which occupations will change? How much cheaper will a particular service become? These are important questions, but AI agents may introduce a deeper economic change that cannot be understood only through hours saved or tasks automated.

The more consequential shift begins when software moves from processing information to participating in action. Conventional software gives people data, forecasts or tools and leaves the next step to them. An AI agent can receive a goal, search for information, use connected systems, compare alternatives, coordinate with other agents and, within an authorized mandate, perform part of a transaction or workflow.

That distinction matters because an economy is not simply a volume of goods and services. It is also an enormous coordination system in which people search, compare, contract, verify, negotiate, allocate resources and decide what happens next. If some of those actions can be delegated to software, the result may be more than another wave of automation. It may alter the economics of coordination itself.

Much of the Economy Is Coordination

A remarkable amount of work inside companies does not directly produce the final product. Employees search for information, compare offers, move data between systems, wait for approvals, negotiate schedules, monitor progress and determine who should act next. Individually, these activities can look minor. Across an economy, they consume enormous amounts of time and organizational capacity.

The importance of coordination costs is not new. The economics of the firm has long recognized that companies exist partly because using markets is itself costly. Searching for suppliers, negotiating terms, checking quality, enforcing agreements and coordinating external parties can make it more efficient to perform an activity inside an organization. The boundaries of the firm therefore depend not only on production technology, but also on the cost of organizing transactions.

Agents can change part of that equation. Consider a small manufacturer looking for a new supplier. The process may require market research, supplier identification, correspondence, price comparison, technical screening, risk assessment and contract preparation. A combination of professional agents could handle significant parts of that chain before a person makes the consequential choice.

The human still decides whom to trust, how much risk to accept and whether to enter a major contractual relationship. But the cost of reaching that decision may fall sharply. If that happens across millions of transactions, the macroeconomic effect is no longer simply the sum of employee hours saved. It may change what level of organizational complexity a small firm can afford.

Smaller Firms Can Gain Functional Scale

Scale has always provided companies with important advantages. Large organizations can maintain dedicated teams for research, finance, compliance, procurement, international sales, logistics and customer service. A smaller firm often spreads those responsibilities across a handful of people or performs them at much lower depth.

Professional agents can reduce part of the fixed cost associated with these capabilities. A small firm that can use specialized research, finance, sales, legal-support and coordination agents gains access to some functions that previously required a much larger permanent organization.

This does not mean that a ten-person company suddenly becomes equivalent to a multinational corporation. Large firms still benefit from capital, distribution, brand, data, experience and bargaining power. But the threshold at which a company needs to build a new department may change. Some knowledge-intensive and coordination-heavy functions can become more modular and accessible.

The economic implication is therefore more interesting than simple headcount reduction. Agents may allow greater functional capability at the same organizational scale. For small businesses and small economies, that may be one of the most important channels through which agentic systems create value.

Capital Begins to Acquire Delegated Action Capacity

Industrial machinery increased human physical productivity. Computers increased information-processing capacity. Enterprise software reduced the cost of storing, communicating and coordinating information. In each case, capital largely amplified what people could do.

Agentic systems add a new quality. A productive asset can increasingly participate in the next step of the process itself. An inventory system no longer needs to stop at warning a manager about low stock. An agent can estimate demand, compare suppliers, calculate an appropriate order and, within a predefined threshold, initiate part of the purchasing process. In physical environments, robotic execution can extend that chain further.

BTU’s research framework describes this transition through the concept of agentic capital. The claim is not that software becomes an independent owner, legal person or moral actor. The economic point is narrower: part of capital can now embody not only productive capacity but delegated action capacity.

That means the economically relevant asset is larger than the AI model. It includes the model, organizational knowledge, data, compute, access to tools, protocols, permissions, auditability, professional oversight and, where necessary, robotic execution. A powerful model attached to poor data, a confused process or inadequate controls may produce little durable value.

This also changes how firms should think about AI investment. The relevant question is not only how much computing capacity or how many licenses the company owns. It is how reliably the entire system can execute a useful process, how much coordination cost it removes, how errors are contained and how quickly a problematic action can be stopped or reversed.

Work May Change Inside Jobs Before Jobs Disappear

The most emotionally charged economic question around AI concerns employment. That is understandable, but framing the issue only as a list of jobs that will disappear or survive may obscure the more immediate transformation.

Most jobs contain many different activities. A financial professional finds data, checks it, calculates, interprets, communicates and prepares decisions. A marketer researches customers, develops ideas, produces materials, runs campaigns and interprets results. A lawyer reads documents, compares rules, identifies risks, discusses alternatives and develops a position with a client.

Agents will affect these activities at different speeds. Information search, structuring, comparison, monitoring and repetitive coordination are particularly amenable to delegation. Goal-setting, contextual judgment, trust, difficult relationships, value conflicts and final accountability remain harder to transfer responsibly.

The result can be a redistribution of value inside occupations. People may spend less time performing mechanical preparation and more time reviewing AI-generated work, interpreting unusual cases, designing processes and making decisions. An important future skill may therefore be not simply “using AI,” but knowing how to organize work with agents and challenge their output intelligently.

There is also a distributional question. Higher productivity can increase employee income, reduce prices for customers, raise company profits or generate new rents for the owners of models, platforms, compute infrastructure and protocols. Technology itself does not determine which outcome prevails. Competition, ownership structures, labor institutions and public policy shape how the gains are divided.

Buyers and Sellers May Meet Through Agents

In today’s digital economy, the user still performs most of the economic action. A person opens a website, searches, compares prices, reads conditions and makes a choice. Companies use platforms, advertising and search engines to compete for that person’s attention.

Agentic markets can change this interface. A consumer may ask an agent to find insurance within a specific price range and set of conditions, or to compare travel options while considering previous preferences and free cancellation. In this case, the first system examining the market on behalf of the consumer may itself be another software system.

Companies can also deploy agents that respond to requests, generate offers, calculate delivery terms and negotiate within predefined boundaries. Part of digital commerce could therefore become an environment in which the buyer’s agent and the seller’s agent communicate through shared protocols before a person becomes involved.

The benefit can be substantial: lower search costs, faster comparison and a greater ability to process complex offers. But a new economic question appears immediately. Who determines what the consumer’s agent can see? Which suppliers are accessible to it? Is the agent optimizing for the person’s objective or for the commercial incentives of the platform on which it operates?

Agentic markets can strengthen consumer capability only if the consumer’s delegated agent genuinely acts under the consumer’s mandate.

Protocols Can Become a Source of Market Power

The digital economy has already shown that market power does not come only from owning the product being sold. Platforms that connect buyers and sellers can influence visibility, pricing, access and switching costs.

Agentic systems deepen this issue because agents need technical rules for economic action. They need to know which systems they can connect to, how identities are verified, which data can be exchanged, what authority exists, how transactions are settled and how actions are recorded.

Taken together, these rules create a protocol environment for economic action.

If that environment is open and interoperable, agents can interact across multiple providers and firms can switch technologies more easily. If it is closed, the owner of the gateway may decide which agents can participate, what information they receive, how offers are ranked and what firms must pay to reach the user.

BTU’s agentic-economy framework describes this as a form of protocol power. Traditional economic power derives from assets, market share and information. Agentic markets can add another source: the ability to define the rules under which economic action is technically permitted.

This is why interoperability and data portability may become economic competition issues rather than merely engineering preferences. A market can contain many sellers and still be concentrated if access to the buyer’s agent depends on one controlled technical gateway.

Trust Has to Become More Verifiable

Traditional commercial trust is supported by reputation, contracts and legal responsibility. In agentic systems, the path from intention to action may involve several components: one agent gathers information, another checks conditions, a protocol validates authority, a platform processes payment and a person enters only above a specified threshold.

When something goes wrong, the final outcome is not enough. The organization needs to reconstruct how the outcome was produced.

What goal did the agent receive? Which data did it use? Under what mandate did it act? Which limits were checked? Where did human approval occur?

Audit trails and tracing therefore become part of economic trust infrastructure. Their purpose is not simply to identify blame after an incident. They also make it possible to improve the system, narrow an agent’s authority and prevent a repeated error.

BTU’s research framework treats auditability and human sovereignty as productive elements of the agentic economy rather than external constraints added after the technology is built. If a company cannot trust an agent enough to delegate meaningful action, much of the potential economic value of the technology disappears. Trust, in this setting, increasingly means the ability to observe, stop, reconstruct and correct action.

Productivity Gains Will Not Distribute Themselves

If agents reduce coordination costs, expand the functional capacity of small firms and improve productivity, the next question is unavoidable: who receives the gain?

Employees may create more value in the same amount of time and capture some of it through higher income. Competitive markets may pass efficiency gains to consumers through lower prices. Firms may retain more profit. Or a substantial share of the surplus may accrue to the owners of models, cloud infrastructure, platforms and protocols on which agentic operations depend.

None of these outcomes is dictated by the technology itself.

AI creates a new productive possibility. Ownership, competition, labor markets and public policy determine how the value created by that possibility is distributed.

For this reason, measuring the agentic economy only through company-level ROI will eventually be insufficient. Economies will also need to understand who owns the model and infrastructure, who controls critical data, whether companies can switch providers, who sets the protocol and whether productivity gains appear in wages, prices, profits or platform rents.

BTU Researchers’ Assessment

According to BTU researchers, the deepest economic effect of AI agents may be understood not simply as labor automation but as the transformation of economic action into something that can be partially delegated to capital. Industrialization amplified physical action, the digital economy dramatically reduced the cost of processing and transmitting information, and the agentic economy adds another layer: software can carry part of an authorized economic process forward.

Several consequences follow simultaneously. Capital acquires a new action-oriented quality; some coordination and transaction costs can fall; smaller firms may gain access to broader professional capabilities; tasks are redistributed within occupations; consumer and company agents can become new intermediaries in markets; and control over protocols and infrastructure may become a new source of economic power.

From this perspective, an agentic economy is not simply the existing economy equipped with better software. Its distinctive feature is that organized production and exchange gain a new operational participant. A software agent does not thereby become a legal person or an independent bearer of moral responsibility, but its actions can affect prices, resources, timing and transactions. Economic analysis therefore needs to separate operational agency from legal status and final accountability.

For Georgia, this distinction is strategically important. A small economy does not need to build every frontier model in order to create local value. It can combine global AI capabilities with Georgian knowledge, sector data, professional agents, local business processes and trusted rules for delegated action. The opposite outcome is also possible: Georgia may become primarily a buyer of foreign agent services while control over models, cloud infrastructure and protocols – and a large share of the economic surplus – remains elsewhere.

According to BTU researchers, the policy question is therefore not only how quickly an economy adopts AI. A more important question is what capacity for economic action is being created locally, who owns or controls that capacity, and how the resulting productivity gains are distributed.

Key Findings

• The defining economic shift is from information processing toward bounded machine participation in economic action.
• Agents can reduce parts of search, coordination and transaction costs.
• Smaller firms may gain functions that previously required greater organizational scale.
• Agentic capital includes models, data, knowledge, compute, tools, mandates, auditability and human control rather than the model alone.
• The first labor-market effects may occur through the redistribution of activities within occupations rather than the immediate disappearance of entire professions.
• Buyer and seller agents can change search, comparison, negotiation and digital-commerce architecture.
• Control over protocols, data and access to agents can become a new source of market power.
• Auditability and human control become part of economic trust infrastructure.
• Productivity gains are not automatically distributed fairly; the outcome depends on competition, ownership, labor markets and institutions.
• Georgia’s opportunity lies in expanding the functional capacity of small firms, while dependence on external models, cloud infrastructure and closed protocols is a strategic risk.

Why This Matters for Georgia

For a small economy such as Georgia, agentic transformation is both an opportunity and a strategic choice. One of the constraints faced by smaller firms is organizational scale: it is expensive to maintain deep specialist capabilities in research, finance, international sales, compliance, legal support and project coordination. Professional agents can lower part of that cost and allow smaller teams to prepare and manage decisions that once required much larger organizations.

The value created by this shift, however, will not automatically remain in Georgia. If core models, cloud infrastructure, agent marketplaces, data platforms and communication protocols are all externally controlled, Georgian firms may become more productive while simultaneously becoming more technologically dependent. Georgian knowledge systems, sector data, professional agent-building capability, cybersecurity, data portability and interoperability therefore become questions of economic competitiveness.

Georgia’s advantage should not be measured only by how quickly it adopts global AI platforms. The more important test is whether global technology can be converted into locally controlled economic capability. A useful formula is global AI capability + Georgian knowledge + professional agents + trusted local data + interoperability + human accountability.

Conclusion

AI agents are not changing the economy simply because they can write, calculate or search more quickly. Those are important productivity improvements, but the deeper shift begins when software becomes part of the chain of economic action – finding a partner, comparing an offer, checking a condition, allocating a resource, preparing a transaction or carrying out part of a process under an explicit mandate.

Once this happens, several long-standing economic boundaries begin to move at the same time. Capital acquires a new action-oriented quality, work is redistributed within occupations, smaller firms can manage more functions, new digital intermediaries appear in markets, and control over protocols and infrastructure becomes a new source of power.

The defining economic question of the agentic era will therefore not be only how intelligent AI becomes. It will be how much economic action can be delegated, how far coordination costs fall, who controls the infrastructure through which that action occurs, and who ultimately receives the productivity gains it creates.

Data and Main Sources

• BTU research and analytical work on the AI agentic economy, agentic capital, economic actors, coordination costs, protocol markets and economic power.
• Gondauri, D. & Batiashvili, M. – AI Agentic Economy: Humans, AI Agents, Robots and Protocols in the New Economic Order, BTU research materials, 2026.
• Gondauri, D. & Batiashvili, M. – research materials on Agentic Capital, 2026.
• Ronald Coase – The Nature of the Firm.
• OpenAI official guidance on building AI agents and Agents SDK materials.
• Anthropic – Building Effective Agents.
• National Institute of Standards and Technology – AI Agent Standards Initiative.
• European Commission – AI Act regulatory framework.

This material is analytical and educational in nature. It does not constitute specific technology, financial, investment, tax, legal or economic-policy professional advice.

Prepared by the academic team of Business and Technology University and the BTUAI Research Team, Tbilisi, Georgia.