What is the agent economy?
The term “agent economy” describes a world in which software agents do more than answer questions or automate fixed workflows. They observe information, pursue goals, coordinate with other systems and, within defined limits, take actions on behalf of people or organisations. In practical terms, that could mean comparing suppliers, booking logistics, monitoring contracts, adjusting inventories, triaging customer requests or negotiating service parameters across digital platforms.
This is not a wholly new idea. Economists and computer scientists have long studied autonomous software in markets, from algorithmic trading to auction design and multi-agent systems. What has changed is the breadth of capabilities now becoming available through large language models, improved planning techniques, tool use and easier integration with enterprise software. Agents are starting to look less like scripts and more like flexible intermediaries between human intention and machine execution.
The agent economy is not merely about smarter software; it is about software acquiring enough initiative to participate in markets rather than simply support them.
That distinction matters. A chatbot that drafts an email is useful, but it does not alter market structure. An agent that can source a component, check compliance constraints, compare delivery times, negotiate within budget thresholds and place an order begins to change how firms operate and how digital commerce is organised.
Why now?
Several strands of technology are converging. First, foundation models have widened the range of tasks software can handle, especially those involving language, ambiguity and unstructured information. Secondly, application programming interfaces and cloud infrastructure make it easier for agents to interact with existing systems. Thirdly, advances in model orchestration, retrieval and tool calling have improved the reliability of multi-step actions. Fourthly, the cost of experimentation has fallen: deploying an automated process no longer always requires a bespoke software project.
Research institutions have been tracking these shifts from different angles. Stanford’s 2025 AI Index notes continued progress in model capability and adoption, while the OECD has examined how AI systems increasingly affect work organisation, productivity and trust. Meanwhile, the World Economic Forum and academic researchers have explored how digital ecosystems evolve when coordination costs fall and machine-mediated decisions become more common.
The result is a broader frontier for automation. Earlier software excelled when environments were structured and rules explicit. Agents can operate in messier conditions: reading documents, reconciling conflicting signals, asking follow-up questions and selecting from alternative courses of action. They remain imperfect, but they reduce the amount of human supervision needed for many administrative and commercial tasks.
From assistants to actors
To understand the economic significance of agents, it helps to separate three stages. In the first, software assists humans: summarising information, recommending options or drafting outputs. In the second, software executes bounded tasks once instructed: sending invoices, classifying tickets or updating records. In the third, software becomes an actor with delegated authority: it monitors conditions, chooses when to intervene and completes transactions according to rules and goals set by its principal.
It is this third stage that makes the phrase “agent economy” meaningful. Economic activity depends on search, matching, negotiation, contracting, monitoring and enforcement. When agents can perform some of those functions, they become part of the institutional machinery of markets. They can reduce transaction costs, speed coordination and widen the range of economically viable exchanges.
The agent economy is not merely about smarter software; it is about software acquiring enough initiative to participate in markets rather than simply support them.
Ronald Coase’s classic argument about the firm remains useful here. Firms exist partly because using markets is costly. If agents make market interactions cheaper and more reliable, the boundary between what happens inside the firm and what is outsourced could shift again. Some functions may become more modular. Others may centralise further if firms decide that control, data access and risk management matter more than flexibility.
Where agents may matter first
The near-term impact is likely to be uneven. Consumer-facing agent commerce attracts attention, but business environments are more plausible early arenas because they offer clearer incentives, repeatable processes and better-defined risk tolerances. Procurement, customer support, IT operations, scheduling, compliance checks and back-office administration are all fertile ground.
In supply chains, for instance, agents can continuously compare inventory data, transport options and supplier terms, then recommend or execute routine decisions. In professional services, they may assemble research materials, draft standard documents and manage workflow hand-offs. In healthcare administration, they could assist with coding, appointment logistics and prior authorisation processes, though the governance burden there is particularly high. In finance, they may support internal operations and monitoring more readily than unrestricted front-office autonomy.
What these domains share is not glamour but structure. They contain large volumes of repetitive coordination work, much of it still handled through email, portals, spreadsheets and human follow-up. Agents thrive where latency, friction and inconsistency impose real costs.
The earliest value in the agent economy is likely to come not from theatrical autonomy, but from the quiet removal of administrative drag.
How markets change when software can transact
If agents become routine participants in commerce, market design itself will begin to shift. Search may become less visible to users and more machine-to-machine. Product information will need to be legible not just to humans browsing a website, but to agents comparing options across multiple providers. Pricing may become more dynamic, but also more contested if buyers deploy agents to arbitrate offers in real time. Reputation systems, service-level guarantees and verification mechanisms could become far more important.
This could make some intermediaries more valuable and others less so. Platforms that reduce uncertainty, standardise data and enforce trust may gain power. By contrast, businesses that rely mainly on confusing menus, opaque pricing or high switching costs may find themselves exposed if agents can identify better alternatives quickly.
There is also a deeper question about bargaining. If both sides in a transaction deploy agents, negotiations may become faster and more granular. In theory, this could produce more efficient matching. In practice, it may also create new asymmetries. Agents are only as good as the data, objectives and constraints they receive. Large organisations with richer information and stronger technical capabilities may build more effective agents than smaller rivals or individuals.
The productivity promise and its limits
The case for agents rests heavily on productivity. The International Monetary Fund, the OECD and McKinsey Global Institute have all argued that AI could lift output, though with important caveats about adoption speed, complementary investment and labour-market adjustment. Agents could contribute by reducing the time spent on coordination, monitoring and routine decision-making rather than by replacing all expertise outright.
The earliest value in the agent economy is likely to come not from theatrical autonomy, but from the quiet removal of administrative drag.
That distinction is important because much work consists not of one grand act of judgement, but of many small tasks that consume attention. Arranging meetings, reconciling records, checking status updates, moving information between systems and handling edge cases all absorb productive capacity. Even modest gains in these areas can matter at scale.
Yet the limits are equally clear. Agents still hallucinate, misread context, struggle with novel situations and fail in ways that are hard to predict. Research from institutions such as the National Institute of Standards and Technology has stressed the need for robust risk management, particularly where AI affects rights, safety or material outcomes. Productivity gains are also rarely automatic. They depend on process redesign, training, governance and clear definitions of when humans remain in the loop.
In other words, the economic opportunity is real, but it is mediated through institutions. Agents do not simply land in a workplace and create value on arrival. Their contribution depends on whether organisations can reconfigure workflows around them without introducing unacceptable fragility.
Trust, identity and verification
No agent economy can function at scale without stronger mechanisms for trust. Human markets rely on identity, reputation, contracts and dispute resolution. Software agents require functional equivalents. Who authorised an agent to act? What budget or permissions does it have? Which data sources can it rely on? How is a transaction audited? What happens when an agent makes a harmful or unauthorised choice?
These questions point to the importance of digital identity, authentication, access controls and verifiable logs. The issue is not merely cyber-security, though that matters greatly. It is also institutional clarity. Organisations will need ways to specify delegated authority precisely, monitor behaviour continuously and revoke permissions quickly. Standards bodies and regulators are already grappling with parts of this puzzle through work on AI governance, cyber resilience and digital trust.
The emergence of machine-to-machine commerce could also raise legal questions. Existing contract law can accommodate some automated transactions, but disputes become more complicated when actions emerge from probabilistic systems rather than deterministic code. Liability may fall variously on operators, deployers, developers or institutions depending on context. That ambiguity is manageable in low-risk settings; it becomes harder in highly regulated sectors.
In an agent economy, trust will depend less on whether a system sounds convincing and more on whether its authority, behaviour and accountability can be verified.
Competition and concentration
There is a temptation to imagine agents as inherently democratising because they lower the cost of capability. In some respects, that is true. Small firms may gain access to functions once reserved for larger enterprises with dedicated staff. Individuals may be better able to navigate complex bureaucracies or markets if they can delegate search and paperwork to software.
But the competitive picture is more ambiguous. Agent performance depends on access to data, compute, integration layers and user distribution. Those advantages are not evenly spread. Firms that control critical interfaces, proprietary datasets or embedded business workflows may be able to shape how agents behave and which options they can see. The history of digital markets suggests that reduced transaction costs do not automatically produce open competition; they can also reinforce gatekeeping.
Policy questions therefore arise early. Regulators may need to consider interoperability, portability, transparency in ranking and non-discriminatory access to essential digital infrastructure. The challenge is delicate: too little oversight could entrench concentration, while overly rigid rules could freeze experimentation before useful models of governance emerge.
In an agent economy, trust will depend less on whether a system sounds convincing and more on whether its authority, behaviour and accountability can be verified.
Labour, skills and organisational design
For workers, the agent economy will be neither a simple story of displacement nor one of effortless augmentation. Tasks will be reallocated. Some roles will be narrowed; others broadened. Demand may rise for people who can supervise systems, define objectives, evaluate outputs, handle exceptions and redesign processes. In many offices, the most valuable employees may be those who can translate organisational intent into machine-executable routines while retaining judgement about what should never be automated.
Past waves of digital adoption show that technology often changes the composition of work more than the absolute quantity in the short term. The World Bank and OECD have both documented how labour-market outcomes depend heavily on education, institutional support and the pace at which firms reorganise. Agents are likely to intensify that pattern. The gains will accrue unevenly unless training and managerial adaptation keep pace.
There is also a cultural dimension. Delegating routine decisions to software changes what workers do all day, but it also changes how organisations understand competence and accountability. If an agent drafts the first contract, flags procurement anomalies and escalates risks, human roles shift upward towards oversight and exception handling. That can improve quality and free time. It can also deskill parts of the workforce if basic operational knowledge atrophies.
What good governance looks like
The right governance approach is neither blanket prohibition nor unchecked deployment. A more durable framework begins with categorisation. Low-risk uses, such as scheduling or internal document routing, can tolerate more autonomy. Higher-risk uses, such as medical triage, financial approvals or employment decisions, require stricter controls, human review and detailed auditability.
Several principles are already emerging across credible policy and standards work. Systems should have clear scopes of authority. Actions should be logged. Sensitive decisions should remain reviewable. Data provenance should be tracked where possible. Performance should be evaluated in realistic operating conditions, not only in benchmark tests. Organisations should assume failure modes will occur and design escalation paths accordingly.
This does not eliminate risk, but it makes risk legible. And legibility matters because the economic value of agents depends on confidence. If people and institutions cannot tell what a system is allowed to do, why it did it and how to challenge the outcome, adoption will stall in precisely those sectors where coordination gains could be greatest.
What to watch over the next five years
The most important indicators will not be dazzling demonstrations. They will be more prosaic signs of institutional maturity. Are there accepted protocols for agent identity and permissions? Are procurement systems, customer-service platforms and enterprise software becoming easier for agents to navigate safely? Are regulators clarifying liability and disclosure in automated transactions? Are firms redesigning workflows around agent supervision rather than merely layering agents on top of existing inefficiencies?
Another signal will be whether agents remain mostly inward-facing, improving internal operations, or begin to act widely across organisational boundaries. The latter would mark a more consequential shift: the emergence of genuine machine-mediated markets in which software regularly discovers, compares and contracts with other software on behalf of human principals.
The agent economy, then, should be approached as an institutional transition as much as a technical one. The technology is advancing quickly, but its economic significance will depend on standards, governance, competition and organisational design. If those pieces evolve well, agents could become a durable layer of digital market infrastructure. If they do not, the result may be fragmentation, mistrust and a great deal of costly automation that no one fully controls.
The prudent view is therefore neither exuberant nor dismissive. Agents are unlikely to abolish work or produce frictionless markets. But they may well alter how coordination happens, where value accrues and which institutions govern digital exchange. That is enough to make the subject economically important now, even before the most ambitious visions arrive.
