Agent Economy Stack

AI is propelling the world economy into a new era — one that is fundamentally cybernetic. In this context, “cybernetics” refers to systems that are self-regulating, programmable, and driven by feedback loops, where decision-making and control are increasingly automated. Instead of relying on manual processes and layers of bureaucracy, we are moving toward an economy that is more efficient, AI-powered, and free from unnecessary intermediaries.

Why is this shift happening? It’s the natural outcome of how markets operate: always seeking the efficiency frontier, relentlessly optimizing for lower costs, faster transactions, and greater productivity. As AI agents become autonomous economic actors, they accelerate this drive, automating tasks, reducing friction, and opening up entirely new markets. But with this transformation comes the urgent need for new institutions, protocols, and governance frameworks to support and regulate these AI-driven interactions.

In this article, we’ll explore the architecture of the emerging agent economy. We’ll break down the seven foundational layers — from the compute infrastructure at the base, through the brains and tools of AI, up to the orchestration, financial, and discovery systems that make the whole ecosystem work. Along the way, we’ll highlight how value is captured at each layer, the business models taking shape, and why understanding this stack is crucial for anyone looking to participate in or build for the cybernetic future.

Manual → Cybernetic

The shift from manual processes and human intermediaries — with all their high transaction costs — to automated, AI-powered decision-making is inevitable. Why? Because the economy is constantly striving toward more efficient equilibria. It gravitates toward local minima that maximize productivity while minimizing wasted resources and coordination overhead.

What's driving this transition in practice? Three key factors:

1. Cost reduction: AI agents can perform tasks at a fraction of human cost. What once required teams of specialists can now be accomplished by a single AI system working continuously without breaks or salary demands at superhuman serial speeds.

2. Transaction efficiency: By removing intermediaries and automating verification processes, technologies such as AI and smart contracts reduces the costs of economic interactions — from simple purchases to complex business negotiations.

3. Autonomous operation: AI agents can independently execute tasks, make decisions based on predefined parameters, and even leverage continious reinforcement learning to optimize their performance over time without human intervention.

The markets being transformed are enormous. Consider the agency market — designers, filmmakers, marketing specialists, and website builders — where AI can deliver similar or superior results at a fraction of the cost. When Claude Code or Loveable can build prototypes overnight that once required teams of specialists, businesses take notice. It's happening now, with hundreds of billions of dollars of economic activity already shifting to AI-powered alternatives.

Let's dive deep into the infrastructure layer and dissect the essential components that make this transformation possible. Understanding these layers is crucial — they are the foundation upon which the entire agent-driven economy is being built.

The Agent Economy Stack

Cybernetic agent economy relies on a stack of interconnected technological layers. For each layer, let's take a look at the value capture mechanisms and business models that are emerging in this new landscape.

Layer 0: AI Compute Layer

The foundation of the agent economy: fundamental computing infrastructure enabling AI model execution and training.

Components:

• Verifiable Inference: Systems proving AI computations executed correctly, building trust in outcomes without revealing underlying data or models.

• Decentralized Inference: Distributed networks performing AI tasks across many nodes, enhancing resilience and reducing reliance on single providers.

• Cloud Inference: Cloud platforms and hyperscalers optimized for high-performance AI execution.

• Training Infrastructure: Specialized systems and datacenters designed for the computationally intensive process of pre- and post-training models.

Value Capture: Value is primarily captured through compute usage fees (pay-per-use).

Layer 1: Foundational AI Blocks Layer

The brains of the system: core AI models, fine-tunes, and infrastructure powering agent capabilities.

Components:

• Large Language Models: Advanced AI capable of understanding, generating human-like text, multimodal output and performing complex reasoning tasks — the general-purpose foundation.

• Fine-tuned Models: LLMs specialized for particular domains or tasks (e.g., medical diagnosis, code generation, cooking), trading generality for enhanced performance in specific contexts.

Value Capture: The dominant model is pay-per-use via API calls. Enterprise licensing offers another route, allowing organizations to host models internally, often with custom fine-tuning for proprietary needs.

Layer 2: Tools & Capabilities Layer

The hands and senses of the system: extensions allowing AI to interact with the world beyond text.

Base AI models often lack the ability to perform specific actions or access real-time information. This layer provides the tools and interfaces needed to bridge that gap, enabling agents to execute tasks in the digital and physical world. Tools solve specific problems (e.g., BrowserUse for headless web navigation, ElevenLabs for voice synthesis).

Components:

• AI Tools & Plugins: Modular functions extending base model capabilities, such as web search, code execution, image generation, or specific API interactions.

• MCP Servers and A2A: Standardized interfaces simplifying how agents access and utilize various capabilities, potentially becoming the "microservices" equivalent for AI.

• Agent Capabilities: Specialized functions allowing agents to interact reliably with external systems (e.g., booking systems, financial APIs, IoT devices).

Value Capture: Value is captured through fees for tool usage, often via API keys managed by the end-user or agent developer. Commercial tools often compete with open-source alternatives on reliability.

Layer 3: AI Agents Layer

The workers of the economy: autonomous entities with specialized skills and decision-making capabilities.

These are the applications or systems that leverage foundational models and tools to achieve specific goals autonomously. They range from simple task-specific bots to complex, multi-skilled digital entities.

Components:

• Specialized Agents: AI systems designed for specific domains (e.g., legal contract review, financial analysis, travel planning) with deep expertise.

• Agent Frameworks: Development platforms (e.g., LangChain, AutoGen, Crew, Mastra, etc.) simplifying the creation of agents with specific capabilities and operating parameters.

Value Capture: Monetization mirrors traditional service models:

• Subscriptions: Recurring fees for ongoing access to an agent's service.

• Pay-per-task: A fixed fee for completing a defined job, regardless of complexity (akin to project-based work).

• Pay-per-output: Payment only upon successful task completion or desired outcome, shifting execution risk to the agent. This model relies on the agent's autonomy to assess risk and invest its own resources (compute) with no guaranteed return.

• Deployment Fees: Agent frameworks often charge for deployment and hosting, similar to serverless platforms, with costs scaling based on usage, complexity, and resource consumption.

Market Opportunity: The addressable market is vast, disrupting traditional agency work (design, marketing, development) and SaaS models by offering comparable or superior services at significantly lower costs.

Layer 4: Trust & Governance Layer

The institutions of the AI economy: systems enabling reliable transactions and interactions between agents and humans.

Components:

• Communication Protocol: Standards for secure and efficient information exchange between agents (moving beyond simple text/JSON towards more machine-native formats).

• Identity System: Methods for authenticating and verifying AI agents, crucial when granting permissions to access resources like bank accounts or personal data (e.g., passing KYC).

• Reputation System: Mechanisms tracking agent reliability and performance history, acting as a "credit score" to inform trust decisions.

• Cybernetic Contracts: Automated agreements defining interaction terms, conditions, and potentially dispute resolution mechanisms between agents or agents and humans.

• Benchmarks / Evals: Standardized methods for evaluating agent performance and capabilities, either through explicit tests or implicit market success.

• Privacy: Protocols and techniques ensuring sensitive data handled by agents remains protected according to user preferences and regulations.

• Cybersecurity: Security measures, guardrails, and monitoring to prevent agent exploitation, unauthorized access, or malicious activity.

Value Capture: Direct monetization is less clear, but this layer is foundational for enabling value capture elsewhere. Providers of robust identity, reputation, or security solutions will be critical. Insurance markets specific to AI risks will likely emerge, driving demand (and creating value) for verifiable trust and safety features.

Layer 5: Financial Systems Layer

The banks and markets of the agent economy: infrastructure enabling economic activity and value exchange.

As agents become economic actors, they need systems to hold value, transact, and participate in financial activities. This layer provides the necessary financial plumbing.

Components:

• Payment System: Methods for transferring value between agents, humans, and services, potentially leveraging both traditional and blockchain-based rails.

• AI DeFi: Agent-centric financial services like lending (using future agent revenue streams as collateral), borrowing, insurance, and investment products (e.g., ETFs of agent revenue tokens).

• Agent Tokenization: Representing agent ownership, governance rights, or future cash flows as on-chain tokens, enabling new investment and financing models.

Value Capture: Value can be captured through transaction fees, spreads on financial products, asset management fees for agent-based investment vehicles, and premiums for AI-specific insurance.

While nascent, this layer holds significant potential. Once agents consistently generate predictable revenue streams, tokenizing these streams can create new asset classes. Financial products built around these agent-generated cash flows (indices, derivatives) could become commonplace. Web3 infrastructure appears well-suited, but widespread adoption depends on agents first achieving significant economic productivity.

Layer 6: Orchestration & Routing Layer

The managers of the agent economy: systems coordinating complex multi-agent workflows and directing tasks.

Many complex tasks require the collaboration of multiple specialized agents. This layer provides the intelligence to break down tasks, assign them to the appropriate agents, and synthesize the results.

Components:

• Routing System: Mechanisms directing tasks to the best-suited agent(s) based on capabilities, cost, reputation, and availability.

• Orchestration System: Tools managing the execution flow of complex tasks involving multiple agents, ensuring dependencies are met and results are integrated.

• Multi-agent Systems: Frameworks enabling networks of agents to collaborate dynamically to solve problems beyond the scope of any single agent.

Value Capture: Value is captured by charging fees for complex task coordination, potentially based on the number of agents involved, the complexity of the workflow, or a percentage of the value generated by the completed task.

This layer adds value by efficiently managing complexity. For example, planning a complex trip might involve an orchestrator coordinating separate agents specializing in finding attractions, booking flights based on calendar availability, checking visa requirements, and arranging local transport, charging a fee for the successful execution of the multi-agent workflow.

Layer 7: Discovery & Search Layer

The marketplaces of the economy: systems for finding, accessing, and interacting with agents and services.

Users (human or AI) need ways to find the right agent or service for their needs. This layer provides the interfaces and search mechanisms to navigate the agent landscape.

Components:

• Search and Discovery System: Tools for locating and evaluating agents based on capabilities, reputation, cost, and other criteria.

• AI-Native UX: Interfaces designed specifically for interaction with AI agents, moving beyond traditional GUIs.

• AI Intent Resolution: Systems translating vague user requests ("Plan a fun, cheap weekend getaway") into specific, actionable tasks assigned to appropriate agents.

• Memory and Privacy:Systems for storing user preferences and interaction history securely to personalize future interactions while respecting privacy.

Value Capture: Monetization could resemble existing marketplaces or exchanges: listing fees, transaction cuts (success-based fees), subscription access for advanced search/discovery features, or charging agents for prominent placement.

Agent Economy is Inevitable

The shift towards an agent-driven economy is accelerating, fueled by compelling economic advantages. However, this transition requires building new digital infrastructure. The one where AI agents can become independent economic actors, making the economy more efficient, programmable, cybernetic. Such system will inevitably replace the less efficient one, relying on manual and biased decision-making and hierarchical structured with low serial speed.

The ultimate promise extends beyond merely automatation. By drastically lowering transaction costs and enabling complex coordination at machine speed, AI agents will unlock entirely new forms of economic interaction and organization. Building, deploying, and orchestrating these agents effectively is the key challenge and opportunity of the coming decade.