Agentic AI and the Coming Economic Transformation
#125: From automation to autonomous coordination — and the remaking of the social contract
In just a few years, artificial intelligence has evolved from a narrow productivity enhancer into something far more systemic — a technology of coordination. What began as autocomplete for language or code now increasingly resembles autonomous cognition: systems that can perceive, reason, plan, and act in pursuit of goals.
This transition marks the birth of what economists and computer scientists are calling the agentic era — a world in which AI agents don’t just assist human decision-making but increasingly participate in it.
Agentic AI refers to systems capable of autonomous action, able to decompose goals, sequence decisions, and interact with other humans or machines in pursuit of defined outcomes.
Where previous waves of automation replaced tasks, agentic systems reconfigure entire processes. They introduce a new layer of digital intermediaries — economic participants that can negotiate, transact, and adapt on behalf of humans.
As MIT economists John Horton, Andrey Fradkin, Peyman Shahidi, Gili Rusak, and Benjamin Manning argue in The Coasean Singularity? Demand, Supply, and Market Design with AI Agents (2025), these systems promise to collapse transaction costs — the frictions that once defined markets, firms, and organizational boundaries.
If automation was about replacing muscle, and computation about replacing calculation, agentic AI is about replacing coordination.
And coordination, as Ronald Coase observed, is what gives rise to firms, hierarchies, and much of the structure of the modern economy. When coordination becomes programmable, the logic of economic organization itself begins to shift.
The Basic Dynamics Unfolding
Three forces define the early phase of this transformation:
The Dematerialization of Labor.
The marginal cost of digital labor — the cognitive and coordinative work that underpins most white-collar professions — is falling toward zero. Agents can write, schedule, negotiate, and analyze at scale, continuously and without fatigue.
The Programmability of Coordination.
Economic interaction, once limited by human attention and bureaucracy, is becoming machine-executable. Agents can search markets, evaluate trade-offs, and transact directly — in real time. This mirrors what Horton et al. call a “Coasean inversion”: when it becomes cheaper to coordinate through markets (via agents) than within firms, organizational boundaries dissolve.
The Recomposition of Institutions.
As agents take on economic agency, we are witnessing the early erosion of traditional institutional roles — managers, brokers, analysts, and even regulators. The unit of productivity is no longer a worker-hour but a workflow loop: a cycle of perception, reasoning, and action, executed at machine speed.
Together, these dynamics suggest an economy that behaves less like a collection of firms and more like a network of agents — human and artificial — continuously contracting, learning, and optimizing.
A Glimpse of the Emerging World
What might such a world look like?
In the near future, firms may shrink while markets expand. A company might consist of a handful of humans directing fleets of specialized agents that handle design, logistics, finance, and compliance. Competitive advantage will come not from headcount but from architecture: how effectively a firm can orchestrate its agents and align them with strategic intent.
At the same time, new forms of inequality will likely emerge. Early adopters — firms and individuals who learn to integrate agents effectively — will see exponential productivity gains. Others may find themselves disintermediated, not by cheaper labor abroad but by autonomous systems that outperform them locally.
Markets will become more fluid, but also more opaque. Agents negotiating with other agents may settle on prices or outcomes humans can’t fully interpret. The challenge of the coming decade will not just be automation, but comprehension: how to govern, audit, and trust economies that increasingly run at cognitive speeds beyond human grasp.
As Hadfield and Koh (2025) observe, “when the cost of cognition falls, the design of institutions must rise.” The agentic transition will therefore test not only our technologies but our capacity for institutional innovation — in markets, firms, and governments alike.
The Shift from Tool to Teammate
The first generation of AI tools — ChatGPT, Midjourney, Copilot — were largely reactive: you asked, they answered. Agentic AI represents a deeper shift. These are systems that can plan, reason, and act autonomously, sometimes managing entire workflows.
Early versions include frameworks like ReAct and AutoGPT, which allow language models to interleave reasoning and actions — querying APIs, searching the web, executing commands, and chaining subtasks together. Platforms such as OpenAI’s Assistants API or open-source frameworks like LangChain and AutoGen push further, turning language models into digital employees that can manage tools, retrieve knowledge, and even delegate to other agents.
If traditional AI was about intelligence within tasks, agentic AI is about intelligence across tasks. It’s not a hammer — it’s a foreman.
What the Evidence Already Tells Us
Before projecting forward, it’s worth grounding in what we already know about AI’s economic effects.
Studies from the International Labour Organization and the IMF show that up to 40% of jobs could be affected by generative AI — but most will change, not disappear. Rather than full automation, we’re seeing task-level substitution: AI handles subtasks while humans retain oversight.
Empirical work by Noy & Zhang (2023) and Mollick et al. (Wharton) finds significant productivity gains among professionals using large language models. Boston Consulting Group consultants completed 25% more tasks and 40% faster when aided by GPT-4; programmers using GitHub Copilot finished coding tasks up to twice as fast with fewer errors.
Newer studies deepen this picture. Rusak, Manning, and Horton (2025) show that AI agents can also enhance market design itself: by eliciting preferences and coordinating exchanges that were previously too complex or costly, they enable “superior markets” that improve allocative efficiency. This suggests that AI doesn’t just accelerate production — it can improve economic coordination.
The key pattern: when humans and AI collaborate, productivity rises — but so does inequality. Firms that adopt early capture the largest benefits, while laggards fall behind. Larger organizations with the data and infrastructure to integrate AI deeply are already consolidating advantage — what Korinek (2025) calls “the agentic productivity gap.”
The Agentic Leap
Agentic AI accelerates these dynamics dramatically. Once machines can not only generate ideas but also implement them — drafting code, negotiating contracts, or reallocating capital automatically — the marginal cost of digital labor collapses.
This aligns with the Coasean Singularity hypothesis: as transaction costs fall toward zero, the traditional boundaries that separated firms, departments, and even markets blur. Agents can transact directly with each other, continuously searching, negotiating, and executing — effectively turning coordination into a computational process.
Imagine a marketing department where agents autonomously analyze competitors, design ads, test messaging, allocate budget, and iterate in real time. Or a logistics chain where agents forecast demand, negotiate freight rates, and reroute shipments when ports are congested. Humans remain in the loop — but increasingly as supervisors, auditors, and strategic architects rather than executors.
As Tomašev et al. (2025) note in Virtual Agent Economies, this kind of agent-to-agent interaction could create a new layer of economic activity — markets populated primarily by digital entities. The “speed of coordination” becomes a competitive advantage; the economy begins to behave more like a distributed algorithm.
How Organizations May Transform
As agentic systems take on multi-step decision processes, firms will need to reorganize around coordination, oversight, and safety rather than execution. Hierarchies may flatten as managers become agent orchestrators — designing workflows and constraints instead of assigning tasks.
New corporate functions are already emerging: AgentOps, alignment teams, safety monitors, and trust auditors, ensuring that agents behave as intended and that decisions remain auditable and transparent.
Drawing from Hadfield & Koh (2025) in An Economy of AI Agents, the most successful organizations will be those that build institutional “guardrails” — governance systems, ethical frameworks, and contractual architectures that allow human and machine agents to coexist productively. The organizational design challenge shifts from efficiency to alignment.
As Herbert Simon once described human firms as “boundedly rational” systems, agentic AI may be the first technology to relax those bounds — enabling near-continuous optimization at the edge of human comprehension.
The New Scarcity: Strategy Abundant, Execution Scarce
One of the most paradoxical consequences of agentic AI is the reversal of scarcity between strategy and execution.
For most of industrial history, strategy was scarce — deep thinking, scenario modeling, and creative planning were constrained by human cognition and time. Execution, by contrast, was scalable: once a strategy was chosen, armies of workers, machines, and processes carried it out.
Agentic AI inverts that equation. With generative and reasoning models able to generate strategic options, forecasts, and entire business plans in minutes, strategic ideation becomes abundant. The bottleneck shifts to implementation: aligning, monitoring, and verifying thousands of autonomous or semi-autonomous systems executing across networks and supply chains.
This dynamic mirrors what some scholars call the AI execution gap — the growing divide between what can be imagined and what can reliably be done. The abundance of synthetic cognition doesn’t automatically translate into effective action; it requires orchestration, trust, and infrastructure.
In the agentic economy, therefore:
Ideas are cheap; integration is costly.
Vision is easy; verification is hard.
Planning proliferates; reliable execution consolidates.
This is why new managerial disciplines — AgentOps, alignment engineering, and oversight design — are becoming central. Execution becomes the new strategic moat. The firms that can continuously translate abundant AI-generated ideas into robust, coordinated outcomes will define the next industrial elite.
As Alex Pawlowski (2025) argues in Agentic Strategy: Leading Organizations That Think, Learn, and Act, the frontier of competitiveness will not lie in having more intelligence, but in “architecting intelligence into action.” The advantage shifts from strategic originality to agentic coherence — how effectively an organization can turn reasoning into repeatable, auditable, adaptive behavior across its ecosystem of agents.
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