From Tools to Economic Actors
The first era of the internet digitized information. The second connected people. The third may activate software itself as a primary economic actor. AI agents that can perceive, reason, plan, and execute complex tasks autonomously represent something different from prior automation waves. They are not scripts following predetermined rules. They interpret ambiguous goals, formulate multi-step plans, and adapt strategies based on changing conditions.
AI Agent Market Size
Projected market value, 2025-2030 (33-46% CAGR)
Sources: MarketsandMarkets, BCC Research, PixelBrainy (2025-2026 estimates). Some projections reach $250B+ by mid-2030s.
The distinction between automation and agency is structural. A script that scrapes data is a tool. A program that executes a trade at a price threshold is automation. An AI agent tasked with "finding the best inflation hedge" can search financial databases, run macroeconomic simulations, analyze market sentiment, execute trades across platforms, and adjust its portfolio in real time. No individual step requires human intervention. The delegation is of a cognitive function, not just a mechanical one.
Enterprise AI Agent Adoption
Key metrics, 2025-2026
Sources: Gartner (2025-2026), Accelirate enterprise survey. "Apps embedding agents" = Gartner forecast for enterprise applications with task-specific AI by 2026.
The Coasean Inversion
Ronald Coase's 1937 theory of the firm proposed that companies exist because transaction costs in the open market (finding suppliers, negotiating contracts, monitoring quality) exceed the cost of performing those functions internally. When it becomes cheaper to hire employees than to contract externally, firms grow. When the reverse is true, firms shrink.
AI agents may invert this logic. When an agent can discover, vet, negotiate with, and execute contracts with other agents in seconds, the transaction costs that justify large organizations approach a fraction of their current levels. The economic argument for keeping thousands of employees in rigid departmental structures weakens.
The Coasean Shift
How AI agents reduce the transaction costs that justify the firm
| Function | Legacy cost | With agents | Mechanism |
|---|---|---|---|
| Supplier discovery | High | Near zero | Agents search, vet, compare across markets |
| Contract negotiation | Medium-High | Low | Agent-to-agent negotiation (A2A) |
| Quality monitoring | High | Low | Continuous automated verification |
| Market research | Medium | Near zero | Real-time data synthesis across sources |
| Coordination | High | Low | Multi-agent orchestration frameworks |
Framework: Coase (1937). Application to AI agents: Berkeley Haas (2025), NBER working papers (2025-2026).
When transaction costs collapse, economic activity can shift from centralized hierarchies toward networks of specialized agents that assemble and dissolve as needs change.
This does not mean all firms vanish. Berkeley Haas research (2025) notes that AI agents may also enable consolidation, particularly if a few dominant platforms control the orchestration layer. The outcome may be bifurcated: some industries disaggregate into fluid networks while others centralize around platform gatekeepers. The debate is not settled.
What a Firm Could Look Like
A product launch in an agent economy might involve contracting a market analysis agent from one provider, a brand identity agent from another, a swarm of distribution agents from a third, and a supply chain logistics agent from a fourth. These agents coordinate through standardized protocols (MCP, A2A), negotiate resource allocation, and execute strategy. The "firm" becomes a temporary, task-specific nexus of contracted agentic services, dissolving and reforming as needs change.
While AI agents can reduce micro-level transaction costs, their fragmented adoption within a firm can lead to duplicated efforts, conflicting processes, and loss of coherence. Gartner warns that over 40% of agentic projects risk cancellation by 2027 if organizations fail to implement governance, observability, and human-in-the-loop controls. A new class of "Guardian agents" is emerging to oversee autonomous systems, expected to account for 10-15% of the agentic market by 2030.
The Labor Recomposition
The agent economy does not simply "automate" existing jobs. It commoditizes entire cognitive workflows. The work of a junior analyst, a paralegal, a market researcher, or a mid-level project manager can be decomposed into information-gathering, synthesis, and execution steps that agents handle efficiently. The economic pressure to unbundle these roles is real.
The value of human labor may shift toward tasks that resist commoditization: high-level strategic direction, genuine creative ideation, complex ethical judgment, and deep empathetic connection. The premium lands on the ability to ask the right questions, define goals that agents pursue, and provide context that data alone cannot capture.
The "true cost" of AI agents can be significantly higher than LLM token costs suggest. Enterprise deployments face substantial expenses in data preparation, legacy system integration, security, compliance, and ongoing maintenance. This total cost of ownership determines whether firms should build or buy agentic capabilities.
From Attention to Intention
For three decades, the internet has been built around capturing human attention. Interfaces, content, and business models all optimize for eyeballs. The agent economy operates on a different axis. Agents do not have attention to capture. They have objectives. They navigate through APIs, structured data, and semantic metadata to fulfill goals with minimal friction.
This creates pressure to rebuild digital infrastructure. A hotel booking service's value shifts from its photo gallery to whether a travel-planning agent can query availability, pricing, and attributes through a single API call. A news service's relevance depends on providing semantically tagged, machine-readable content that a research agent can ingest, not on clickbait headlines.
A web built for agents is one where structured data outweighs unstructured prose, cryptographic proof outweighs unsubstantiated claims, and efficiency outweighs engagement.
This shift could, paradoxically, produce a more honest digital commons. Agents, being logical and goal-driven, are less susceptible to dark patterns, misinformation, and sensationalism. They favor reliable sources, verifiable data, and transparent services. Economic incentives realign from capturing human attention to servicing machine intention.
The Risks
Concentration. The companies that control foundational models and orchestration platforms could create monopolies of unprecedented scale. If agents become the primary interface for economic activity, whoever controls the agent layer controls the access point.
Systemic fragility. A cascading failure in interconnected financial agents could trigger a market crash in seconds. The speed of agent-to-agent interaction means errors compound exponentially before humans can intervene.
Accountability gaps. When an autonomous agent causes harm, is the user, the deploying company, the model trainer, or the data provider responsible? Existing legal frameworks, built around human actors and corporate entities, do not have clear answers. New paradigms of algorithmic accountability are needed.
Malicious agents. Agents designed for scams, cyberattacks, or large-scale manipulation could create hostile environments that undermine the efficiency the agent economy promises. Agent verification (KYA, or "Know Your Agent") is emerging as a parallel to KYC.
The agent economy is not a hypothetical scenario. The market is $7.5 billion and growing at 33-46% annually. 79% of enterprises report adoption. The structural pressure on firms, labor markets, and digital infrastructure is measurable. The transition does not eliminate human value; it concentrates it in strategic, creative, and ethical domains that agents cannot replicate. The question is not whether the agent economy arrives. It is whether the institutional, legal, and organizational frameworks adapt fast enough to contain its risks while capturing its potential.