Intent Economy

TL;DR

What? The digital economy is shifting from selling inputs (clicks, hours) to delivering verified outcomes.

So what? Power moves from platforms that sell your attention to AI agents that fulfill your intention.

Now what? This creates huge opportunity for builders to create the infrastructure for intent matching, routing, evaluation, and settlement.

Short summary of this article is available on our YouTube channel (2min).

Introduction

Imagine: you need to renovate your kitchen.

Today, you become an unwilling, unpaid general contractor. Weeks of work ahead: find an architect, vet contractors, source materials from a dozen suppliers, navigate permits, align schedules, manage payments. Forty-seven browser tabs open. Three spreadsheets. Endless phone calls. The project succeeds only if you can manually coordinate dozens of disconnected services.

Tomorrow, you state one intent: "Renovate my kitchen, mid-century modern, $40k, done by June." Your AI assistant breaks this into tasks and broadcasts them to a network of specialized agents. Within seconds, automated auctions run across every step: design, materials, labor, permits. Providers compete on price, speed, quality, and reputation. The best combinations win. Work gets validated automatically. Payment releases on completion. You get progress updates.

The project is managed for you, not by you.

This is the Intent Economy. The shift from selling attention to fulfilling intention. From inputs to outcomes. From platforms that monetize your confusion to markets that compete for your goal.

The digital economy is restructuring around a simple idea: the best interface is the one where you say what you want once, and the system figures out how to deliver it. This article explains why this shift is inevitable, how these systems will work, and what it means for founders building the infrastructure of this new economy.

What’s going on?

The digital economy is undergoing a paradigm shift, moving from a model built on capturing user attention to one centered on fulfilling user intent. This article presents cyber•Fund's thesis that this transition is inevitable, driven by fundamental advances in AI, economics, and cryptography. The emerging "Intent Economy" is a high-speed, automated, and decentralized marketplace where users broadcast specific goals or “personalized RFPs”, and a network of specialized AI agents compete in real-time to fulfill them. This will streamline user experience, increase the market efficiency, disrupt incumbent business models and create new opportunities for founders.

The fundamental unit of economic activity is shifting from inputs (labor hours, software seats, ad clicks) to verified outcomes. Value is captured by autonomous agents that translate user's intent into an achieved well-specified goal.

Today, in the Attention Economy when you make a search query on Google or Amazon, the platform sits between you and the provider. A credibly neutral, decentralized Intention Economy removes this conflict: no single party can see all intents and preferentially route them for profit. Agents compete on their merit and ability to provide the best possible product or service.

The Intent Economy's core mechanism — sophisticated, multi-attribute auctions run by AI agents — is proven to be vastly more efficient than today's posted-price and advertising models.

Definition: Multi-attribute mechanism design is a mechanism that allocates goods or services and determines payments while optimizing across multiple attributes (e.g. price, quality, time), not just a single dimension like price.

The future is not a single "super AI" but a diverse ecosystem of specialized agents, mirroring the division of labor and the software industry's shift to microservices. The scaling of single, monolithic AI models is hitting fundamental data and cost ceilings. The next frontier of AI progress will come from the emergent intelligence of interacting multi-agent systems, similar to diverse evolutionary paths in the biological systems or competing goods in the market capitalism.

The image above shows a century-long pattern: every new business model reduces friction in how people get what they want. The Intent Economy is the next step that minimizes the gap between desire and fulfillment. By shifting from choice overload to direct outcome delivery, it will unlock entirely new categories of products and services, generate trillions in value, and reward the companies that master intent capture and execution.

This new economy cannot function without programmable trust. Otherwise, it risks collapsing into fraud, opacity and centralization (thus, reduced market efficience). Cryptographic technologies like zero-knowledge proofs, MCP communication and A2A coordination protocols for agents, as well as standards like ****ERC-8004 provide the essential, trustless foundation for building secure, scalable, decentralized global AI marketplace.

In this article we will explore in detail: (a) why this shift is inevitable in some areas of the economy; (b) how such systems can be designed; and (c) the open problems and opportunities for founders and researchers to participate in the creation of the Intent Economy.

At cyber•Fund, our mission is to accelerate the development of this open, efficient, and decentralized cybernetic economy. We are investing in the foundational infrastructure of this new agent-driven world, and this document serves as a guide for the founders who will build it.

Attention → Intention

The internet's dominant business models — advertising and e-commerce — are built on capturing and selling user attention. This "Attention Economy" created today's tech giants but is fundamentally inefficient and misaligned with user goals. Power is concentrated in platforms that control user data, making user the product, not the protagonist.

The Intent Economy inverts this dynamic. It is a marketplace where buyers publish explicit intent signals (goals with constraints like budget, time, quality) while suppliers (AI agents) compete to fulfill them on the buyer's terms.

Consider the task of renewing a passport:

• Attention Economy: Hours spent navigating confusing government websites, searching for photo services, and manually coordinating appointments and payments, all while being bombarded by irrelevant ads.

• Intent Economy: The user's personal AI agent is given a single intent: "Renew my US passport"

Instantly, a market forms around this intent. A coordinating agent orchestrates the optimal combination of specialized agents — a Forms Agent, a Vision Agent for photo compliance, a Notary Agent — who bid, execute, and settle the task automatically. The complexity is abstracted away, customers receive superior service and auction mechanism guarantees the best possible terms for the consumer.

Why Intent Economy is happening?

Before going into details of how the Intent Economy will function, let’s consider the arguments for why this shift is likely to happen in technological, economical, political and other dimensions:

Customer experience argument

The fundamental driver of this shift is the radical superiority of the user experience. People don’t want to browse, click, compare, or fill out forms; they want an outcome.

The current digital economy forces users to perform laborious, multi-step processes. To book a hotel, you don’t just state your need; you navigate websites, apply filters, read reviews, and manually compare options. This is cognitive overhead. The Intent Economy eliminates it.

The user experience of the Intent Economy is closer to how a three-year-old interacts with the world: they simply state what they want. The complexity of how it happens is abstracted away. You express a desire — “I want to go on vacation,” “I need to buy a car,” “I want to bake bread” — and the system orchestrates the most efficient path to that outcome. Your personal agent translates your goal, and a competitive market of specialized agents ensures it is met on the best possible terms.

It’s about reclaiming the user’s most valuable asset: their time and focus. By shifting the burden of execution from the user to an automated, competitive system, the Intent Economy offers a 10x improvement in user experience that will make the old way of doing things feel archaic and inefficient.

The economic argument

Game-theoretic imperative

The economy is becoming the RL machine.

AI gains true economic agency — the ability to recognize other agents, model their incentives, and optimize strategies accordingly. This will transform the digital economy into a vast, multi-agent reinforcement learning system. Critically, the substrate on which these games are played must be credibly neutral. Without neutrality, the "referee" becomes a player with privileged information, able to front-run every move.

Mechanism design efficiency

Nobel-winning research proves that well-designed auctions are fundamentally superior for price discovery and efficient allocation in complex scenarios. The Intent Economy is a market of immense complexity.

Structuring interactions as sophisticated, multi-attribute auctions, run at machine speed by AI agents, will incentivize truthful information revelation and ensure the user's intent is fulfilled by the best-equipped supplier at the most efficient price.

AI as a Coasean costs killer

Ronald Coase argued that firms exist to minimize transaction costs (search, bargaining, enforcement). AI agents obliterate these costs (see our previous article on this topic for more details). They can automate search, conduct complex negotiations in milliseconds, and monitor contract fulfillment through automated verification.

Information economics

The current economy suffers from massive information asymmetry. The Intent Economy inverts this. When a user broadcasts a specific, constrained goal, they send a clear, credible signal of their precise intent. This allows specialized suppliers to make tailored, relevant offers, eliminating the waste of the advertising model and leading to a more efficient market.

Lastly, specialization creates massive efficiency gains. An ecosystem of AI agents, each optimized for a narrow function (e.g., travel logistics, code auditing, financial analysis), mirrors this principle and will achieve far greater productivity than any general-purpose model.

The technological argument

The software industry's migration from monolithic applications to microservices is the central modern analogy. A microservices architecture enhances resilience, scalability, and agility. The Intent Economy will be an ecosystem of specialized agents acting as "smart endpoints," making the entire system more robust and innovative.

Complexity theory & "Near decomposability"

As Herbert Simon argued, complex systems are almost universally hierarchical and modular. A decentralized, modular ecosystem of AI agents is inherently more resilient and will evolve faster. This structure allows for fault containment and rapid adaptation, making it the most efficient evolutionary path.

Protocols matured

Standardized agent‑to‑agent messaging (A2A), portable context protocols (MCP), TEEs, verifiable compute, and restaking turn anonymous counterparties into contractable ones. Combined, they provide identity, money, privacy, and enforcement so work can be quoted, executed, verified, and settled between strangers at machine speed.

Political argument

The prevailing Attention Economy operates on a model of surveillance capitalism, where user data is the product. This creates a fundamental misalignment with user interests and societal values favoring privacy and autonomy. The Intent Economy offers a structural shift, moving from a surveillance-based model to one grounded in privacy and user empowerment.

By leveraging cryptographic primitives (zk, MCP, TEE and potentially FHE computation), the Intent Economy can be designed to be privacy-preserving by default. Users can express their intents and prove their qualifications (e.g., sufficient funds) without revealing sensitive personal data. This inverts the current dynamic: instead of platforms monetizing user data, users control their own context and grant access on a need-to-know basis. Power shifts from the platform to the user, who dictates the terms of engagement, creating a more equitable and trust-minimized digital society.

The biological argument

Nature's success is built on diversity and specialization. An economy reliant on a single AI solution creates a fragile "AI monoculture," where a single flaw, bias, or exploit can cause correlated, catastrophic failures — a digital equivalent of the Irish Potato Famine. Evolutionary biology shows that biodiversity is the primary driver of resilience and innovation.

The creation of an open marketplace for intents acts as a new "ecological opportunity," triggering a "Cambrian explosion" of specialized AI agents. A diverse ecosystem of agents, built by different teams on different data, is inherently more robust. It can withstand shocks and adapt faster because failures are contained and multiple evolutionary paths are explored in parallel. A shock to one part of the system is buffered by the variety of other agents, mirroring the stability of a biodiverse rainforest compared to a single-crop farm.

Why Intent Economy is going to be decentralized?

We can refer to two archetypical examples of new digital markets — one being the Apple App Store with its vertically integrated and carefully curated access; the other being the internet itself, fully permissionless and decentralized.

We believe that Intent Economy will bear more resemblance to the latter. A decentralized market allows very small, agile teams to build and deploy state-of-the-art agents for specific sub-tasks, differentiating on dimensions like speed, quality, cost, or compliance. On the other hand, the ability of agents to discover, evaluate, and negotiate at scale without a central intermediary removes a significant bottleneck and cost layer.

Another fundamental reason the Intent Economy must be decentralized is the front-running problem. When you express an intent in a closed, platform-owned system, the platform has both the information and the incentive to extract maximum value from that intent — not by serving you best, but by selling access to the highest bidder.

When you type "buy ergonomic office chair" into Google, the first results aren't the best chairs, but the ones from merchants who paid the most for that keyword. The platform sits between your intent and its fulfillment, monetizing the gap. In a credibly neutral, decentralized Intent Economy, no single party can see all intents and preferentially route them. The system is structured so that agents compete on merit, actual ability to fulfill your intent on optimal terms, rather than on their willingness to pay for access to your attention.

Intent Economy in action

To make this concrete, let's walk through the lifecycle of an intent, from goal to settlement, as illustrated in the diagram below.

Intent Declaration

The whole process starts with the user expresses a goal to their personal AI assistant (e.g. "Plan a 3-day corporate offsite in Lisbon for 10 people next month, budget is $20k, focus on team-building activities", “renew my US passport”, “buy high-quality ergonomic furniture for our new 300 m2 office at the best possible price”). The assistant, using the user's private context (e.g. calendars, past travel, corporate policies), translates this into a structured, machine-readable intent.

The Intent Declaration system aims to transform a user's simple message (via WhatsApp or voice input) into a comprehensive, structured request. This preparation typically occurs through a locally running agent (such as Siri or an Ollama/LMStudio-based personal assistant) or a chatbot that stores the user's private context and connection tools. For instance, a local agent can access the user's calendar to determine the optimal delivery date for a purchase and incorporate this information into the request.

{ "task_id":"form-fill", "bidder_agent_id": 1024, "price": {"asset":"USDC","amount":3.20}, "eta_ms": 1200, "plan": ["parse","fill","sign"], "reputation": { "validation_success_30d": 0.93, "median_score_30d": 92, "jobs": 184 }, "validator_bundle": [ {"validator_agent_id": 77, "quote": {"asset":"USDC","amount":0.25}, "sla_ms": 600} ] }

Orchestration

An Orchestrator agent acts as a general contractor. It decomposes the high-level intent into a dependency graph of sub-tasks: flight booking, accommodation, activity scheduling, compliance checks. For each sub-task, it defines the required outcome and acceptance criteria, creating a machine-readable RFP. It also generates a plan of actions: what 3rd party agents are required to complete the overall task, what context do they need, which ones can be called in parallel and what task dependencies exist.

Task complexity determines the most efficient orchestration strategy. For simple, straightforward tasks, the assistant can execute locally, eliminating the need for market-based matching. Medium-complexity tasks benefit from local orchestration, where the assistant breaks down the intent into sub-tasks but manages the process itself. For highly complex or specialized tasks requiring significant expertise, the assistant outsources the entire orchestration process to specialized cloud agents acting as “managers”, potentially using specialized “Pre-Act” agentic systems.

This process requires a new class of specialized models. Instead of producing direct answers, these systems excel at creating tool-calling chains (or “supply chains” of agents and tools) and executable plans. Recent research, such as the Chain-of-Tools shows Orchestrator's role is not to be an expert in travel and legal compliance, but to be an expert planner. It leverages the deep semantic understanding of LLMs to analyze the sub-task's requirements, determine the exact capabilities needed, and then identify the most suitable agent or tool from a massive, ever-changing pool. This model must also produce a confidence score for its proposed plan, allowing it to flag ambiguity or request clarification before committing resources.

This approach is fundamentally more scalable and robust than monolithic systems. The Orchestrator uses the hidden state, or the rich semantic representation generated by an LLM, to create a query vector for each sub-task. It then retrieves the best-fit agent by matching this vector against a library of tool vectors, each representing an agent's specific function and capabilities. This allows the system to dynamically incorporate new, previously unseen tools without costly retraining. It is a form of hyper-efficient, semantic routing that ensures the right job is always matched to the right specialized agent, moving beyond rigid, fine-tuned models toward adaptable and decentralized network of Intent Economy.

{ "intent_id": "renew-passport-42", "dag": [ {"id":"photo-check","inputs":["photo_cid"],"sla_ms":1500,"accept":{"validators":1,"min_score":90}}, {"id":"form-fill","deps":["photo-check"],"accept":{"validators":2,"min_score":85}} ], "budget": {"max_usd":120} }{ "task_id":"form-fill", "bid_request": { "AgentIDs_allowed":["*"], "TaskSpec_hash":"0xabc...", "sla_ms": 2000, "accept_policy":{"validators":2,"min_score":85} } }

This discovery process can be viewed as a new form of multi-agent gradient descent. The Orchestrator is navigating a high-dimensional space of possible solutions to find the optimal one. Each available agent represents a point in this space, and the matching process—a weighted dot product of the user’s preferences and the agent’s capabilities—is akin to taking a step in the direction of the steepest descent towards the ideal outcome. Preference modeling becomes crucial here: the system maintains a per-user embedding or vector of tastes, which is used to score proposals and can be refined over time by clustering users and learning from cohort data.

Auction

The Orchestrator then submits these machine-readable RFPs as Intent BIDs to a decentralized marketplace. In this environment, a pool of specialized Solver Agents compete by submitting comprehensive proposals, or Solver ASKs. Unlike typical low dimensional price quotes, options (price, expiration date) or real time bidding targeting auctions, these are rich, multi-attribute bids that include price, ETAs, compliance data, verifiable proofs of previous completions, and robust reputation signals.

This process functions similar to RTB markets that power modern advertising, but generalized for any conceivable task. It's a hyper-rational clearinghouse for outcomes. Instead of a user manually comparing posted prices, thousands of agents compete in real-time across a dozen parameters — quality, speed, reputation, security, privacy — to find the true market-clearing price for that specific intent. This forces a "race to the top" on value, ensuring the user receives a guaranteed optimal offer based on their unique preferences, which are programmatically weighted by their orchestrator agent.

For an intent like "buy me a bike for Burning Man trip" the auction must weigh price, delivery date, brand reputation, color, and the solver's own track record. Furthermore, the solvers competing here aren't the bike merchants themselves. They are specialized procurement AI agents, whose core competency is navigating the complexities of e-commerce, logistics, and quality verification to fulfill the user's intent on the best possible terms.

Multi-attribute auction specifics

When an Orchestrator broadcasts an Intent BID, it doesn't just listen for the lowest price. Instead, it uses a utility function to score each incoming Solver ASK based on the user's original priorities. This function is a weighted calculation that might look something like this:

The weights (w _price, w _eta, etc.) are set by the Orchestrator based on the user's stated intent:

• For an urgent task like "I need this legal document notarized in the next 2 hours," the weight for ETA would be extremely high, making speed the dominant factor.

• For a background task like "Optimize my cloud spending over the next quarter," the weight for price/cost savings and the solver's reputation would be much higher.

This mechanism forces Solver agents to compete across multiple dimensions: cost, speed, reliability, and trust. It creates a market that can accurately price complex, outcome-based work and ensures the user's nuanced preferences are reflected in the final match.

Contracting

Once the Orchestrator selects the winning bid, a Cybernetic Contract is formed between the buyer and the solver agent. This isn't a legal document in the traditional sense; it's a self-executing, cryptographically-signed agreement that programmatically enforces the terms of the deal. It is the core primitive that provides trust and guarantees execution in a network of agents.

This contract locks the necessary funds in a smart contract escrow, which will only release payment upon the successful and verified completion of the task. The contract explicitly defines the acceptance criteria: what constitutes a "finished" job. It could be a set of unit tests for a coding task, a specific score from a validator agent, or confirmation of a physical delivery. This automated enforcement mechanism removes counterparty risk and the need for costly intermediaries, allowing for trust-minimized commerce to occur at machine speed and global scale.

{ "task_id": "codegen-feature-login-module-987", "parties": { "buyer_orchestrator": "did:ethr:0x1a2b3c4d5e6f7a8b9c0d1e2f3a4b5c6d7e8f9a0b", "solver_agent": "did:ethr:0x9f8e7d6c5b4a3f2e1d0c9b8a7f6e5d4c3b2a1f09" }, "scope_of_work": { "description": "Generate a Python login module based on spec.", "spec_hash_type": "sha256", "spec_hash": "b94d27b9934d3e08a52e52d7da7dabfac484efe37a5380ee9088f7ace2efcde9" }, "service_level_agreement": { "price": { "asset": "USDC", "amount": 75.50 }, "eta_ms": 3600000, "success_probability_quote": 0.98 }, "acceptance_criteria": { "description": "Code must pass all unit tests and meet quality standards.", "unit_tests": { "required_passed": 17, "total_tests": 17 }, "code_quality": { "cyclomatic_complexity": { "max_score": 5 } }, "validators": { "validator_ids": ["did:ethr:0xvalidator1...", "did:ethr:0xvalidator2...", "did:ethr:0xvalidator3..."], "quorum_required": 2, "min_score": 95 } }, "escrow_and_payment": { "escrow_contract_address": "0xEscrowContractAddress...", "asset": "USDC", "amount": 75.50, "release_condition": "RELEASE upon receiving signatures from 'quorum_required' validators confirming 'acceptance_criteria' are met." }, "dispute_resolution": { "policy": "slash_on_failure", "slashing_terms": { "bond_slashed_on_rejection": "50%", "recipient": "validator_and_buyer_split" } } }

In practical terms, this "cybernetic contract" is effectively a cryptographically signed JSON object. It serves as the single source of truth for the transaction. Crucially, it contains the specific, programmatic rules for unlocking the escrowed funds. It precisely defines what the solver must provide as a proof of completion: a delivery tracking number, a software artifact, or a verifiable credential to receive payment. This tight coupling of the agreement to the escrow and validation steps is what enables the system's trustless nature, ensuring that payment is only released when the contract's conditions are verifiably met.

Execution

Once the cybernetic contract is finalized and the escrow is funded, the Execution phase begins. The winning Solver Agent, now contractually obligated, initiates its task. It acts not merely as a worker but as an autonomous digital general contractor, responsible for delivering the specified outcome. For any non-trivial intent, this rarely involves a single action. Instead, the Solver orchestrates a "supply chain" of other, more specialized agents and tools to complete the job, mirroring the division of labor in the physical economy.

This coordination is not chaotic; it is a programmatic and orderly process governed by standardized frameworks like A2A (for coordination between agents on the market) and MCP (for context portability within the supply chain and subtasks).

As an example, consider an intent to launch a digital marketing campaign. The primary Solver Agent, acting as the campaign manager, would use MCP to call and pay a series of sub-agents in a dependency graph:

1. An Analytics Agent to define the target audience.

2. A Creative Agent to generate the ad copy and video assets.

3. A Media-Buying Agent to programmatically place bids on advertising networks.

Validation

For many tasks, especially high-value or mission-critical ones, an automated validation step is essential to ensure the work meets the contract's acceptance criteria. This is the system's trust-but-verify layer. The cybernetic contract itself specifies the required validation method, which can take several forms:

1. TEE attestation: The solver provides a cryptographic receipt proving a specific piece of code was executed verifiably inside a secure enclave.

2. Rerun: A randomly selected, independent Validator Agent re-executes a portion of the task to confirm the output matches the solver's result.

3. Restaking / slashing: The solver posts a financial bond as collateral. If its work is found to be faulty or malicious by validators, this bond is "slashed," creating a powerful economic deterrent against poor performance.

This process can be governed by emerging cryptographic standards like ERC-8004, which proposes a registry for validators and a standardized format for submitting and verifying AI agents' work. This step produces a verifiable attestation that serves as the final, indisputable proof of completion.

(Optional) Arbitration and dispute resolution

No system is perfect. Agents may get stuck, fail to deliver, or produce results that a user finds unsatisfactory, either justifiably or maliciously. When disputes arise or a solver's work is challenged, the system can escalate to a programmatic and decentralized arbitration process. Instead of relying on a central authority, this process leverages cryptoeconomic incentives to ensure fair and rapid resolution. Systems like Kleros and Aragon Court have pioneered this model, using game theory to create Schelling points where jurors are financially rewarded for voting with the honest majority. This is akin to an Augur-style prediction market for truth, where AI jurors could even be used for low-stakes disputes.

For example, if a user's intent was "create a high-quality landing page" and the submitted work is alleged to be substandard, a dispute can be initiated. A panel of pseudo-anonymous jurors (or autonomous judge agents for lower-cost tasks), who have staked collateral, are randomly selected to review the evidence (the initial intent, the final product, and arguments from both parties) and vote on whether the contract's acceptance criteria were met. Jurors who vote with the eventual consensus are rewarded, while those who vote dishonestly or incoherently risk losing their stake, ensuring a strong incentive for truthful adjudication.

A more sophisticated and potentially more efficient method for resolving complex, subjective disputes involves the use of prediction markets in a model known as futarchy. Instead of jurors voting on what is correct, market participants bet on what a future oracle (perhaps a higher-level court or a specific benchmark) will decide is correct. Market participants would then buy and sell these tokens, with the market price reflecting the collective belief in the probability of a successful outcome. The dispute could be automatically resolved in favor of the outcome whose token price remains above a certain threshold (e.g. 80 cents on the dollar) for a set duration. This transforms validation from a subjective vote into an objective financial market, where those with the most accurate information and highest conviction have the greatest financial incentive to drive the market toward the correct outcome.

Settlement

Upon receiving a positive attestation from the validation layer, the escrow smart contract executes automatically. This is the final, trustless exchange of value. The payment, locked at the time of contracting, is released to the Solver Agent, potentially using payment-integrated standards like x402 to programmatically link the payment to the API call.

The settlement is a two-way process. As the solver receives its funds, the buyer receives the final artifacts associated with the completed task. This could be anything from a deployed landing page and its source code, a voucher for a planned vacation, a delivery tracking number for a purchased item, or a file containing a completed legal document. Both parties also exchange cryptographic attestations confirming the successful conclusion of the transaction, which serve as the raw data for the final step.

Feedback loop

The final stage ensures the cybernetic self-regulation built into the system. The attestations exchanged during settlement are recorded on a public and decentralized reputation graph. This is not a single, simplistic score but a rich, auditable log containing each agent's full execution history, validator scores, client ratings, compliance certifications, and more. This public record becomes an agent's most valuable asset. A strong reputation allows an agent to win higher-value contracts with less collateral, while a poor reputation diminishes its economic opportunities. This creates a powerful, self-perpetuating feedback loop that incentivizes quality, honesty, and reliability across the entire ecosystem, allowing trust to emerge and scale without central authorities.

Challenges, or what can go wrong in the Intent Economy

Multi-attribute auction system is hard The Intent Economy requires balancing price with quality, speed, and reliability in solver selection. Traditional auction mechanisms become exponentially more complex when optimizing across multiple attributes simultaneously, making efficient market clearing a significant technical challenge.

Privacy is key User context such as calendars, contacts, financial data, corporate policies, and private keys — is the foundation of trust of the Intent Economy, and it must never become a commodity. If this data resides on centralized platforms or leaks to third parties, the system collapses into the surveillance capitalism it seeks to replace.

Instead, user context lives in a personal data vault controlled entirely by the user: a local AI assistant, encrypted storage or decentralized web node. Solver agents cannot access this data directly; they can only request specific pieces through authenticated protocols like MCP on a need-to-know basis, with explicit permission. The user's Orchestrator agent acts as a cryptographic gatekeeper, granting temporary, read-only access (e.g., a flight-booking agent can see calendar availability but not event details) and revoking it immediately after the intent is fulfilled. This architectural choice transforms the user from a product into a principal: their data remains their most valuable asset, disclosed only when strategically advantageous.

Reputation and identity is foundation for trust

Without robust, sybil-resistant identity systems and tamper-proof reputation graphs, the Intent Economy becomes vulnerable to fraud. The system's economic security depends on the ability to uniquely identify participants and maintain verifiable performance histories.

In the past, RTB advertising systems faced numerous issues including fraud, opacity, data leakage, and market manipulation. The Intent Economy must address these same challenges with cryptographically verifiable execution and transparent settlement.

New market entrants pose a systemic risk to the Intent Economy: a malicious actor with a blank reputation sheet can execute one high-value scam, extract maximum value, and disappear before consequences emerge.

To prevent this "whitewashing" attack (where bad actors shed their identity and start fresh) the system must implement reputation decay mechanisms that penalize absence of history alongside poor performance, and require new agents to post proportional financial bonds as collateral. An agent with zero track record attempting a $100k contract would need to stake a significant bond; if their work fails validation, this bond is slashed as compensation to the buyer and validator network, creating a powerful economic deterrent.

Over time, as an agent builds a verifiable history of successful, validated completions, they can reduce collateral requirements and access higher-value opportunities with lower friction. This transforms reputation from a simple score into a dynamic, cryptoeconomic signal that gradually unlocks access to the best contracts for those agents who prove themselves reliable over extended periods.

Validators will always collude if they can

We could prevent collusion by randomizing solver selection processes, keeping reserve price logic private, rotating verification participants, and implementing monitoring for statistical anomalies in quote correlations.

In addtion, the system needs to combat adversarial optimization by separating specification from evaluation criteria. Implement rotating test suites, comprehensive execution logging, and penalize overfitting through holdout validation checks.

Validator’s attestations must be treated as cryptographic signatures rather than natural language summaries. One way to do it to require verifiers to independently reproduce results or implement probabilistic verification sampling to ensure proof validity.

The future of AI: heterogenous agents or super-models?

The AI landscape is evolving toward a hybrid architecture that combines specialized tools with unified orchestration. Specialized models and tools deliver immediate value for specific use cases (like Deep Research or web browsing), but these capabilities eventually getting baked back into primary models. Reason: sending gradients within the single network is cheaper than exchanging JSONs across HTTP.

The optimal approach likely combines purpose-built planning agents that can orchestrate specialized tools when needed, while delegating routine tasks to increasingly capable generalist models.

Balancing privacy and trust

Any efficient decentralized system must balance the privacy of personal data with the trust established through verifiable public reputations of market participants.

Private user context

This is the user's sovereign domain. It contains all their personal and sensitive information: calendars, contacts, financial data, corporate policies, and private keys. Data never leaves the user's control. It resides in a personal data vault, a local AI assistant, or a decentralized web node managed by the user.

Solver agents on the public network cannot see this data directly. When necessary, they can request specific pieces of information through authenticated MCP or similar protocol. The user's Orchestrator agent acts as a gatekeeper, fulfilling requests only on a need-to-know basis and with explicit permission. For example, a flight-booking agent can be granted temporary, read-only access to the user's calendar to find open travel dates but is denied access to the details of the calendar events themselves.

Public reputation and verification

Public reputation is stored in a global, transparent, and tamper-resistant ledger where all market activity is recorded. It stores the verifiable facts of the economy: agent identities, their full execution history, attestations from validators, compliance certifications (e.g. GDPR compliance), and reputation scores.

Anyone can query this plane to verify a Solver's track record before engaging with them. A new agent with no history might need to post a large financial bond to be trusted, while an agent with thousands of successfully validated jobs and a high reputation score can win high-value contracts with minimal collateral. This public record makes reputation a quantifiable and portable asset, creating strong incentives for agents to perform honestly and effectively.

Intents in DeFi

While the full vision of a global Intent Economy is still emerging, we can see its early manifestations in the world of DeFi). The most advanced and adversarial economic environment in the world has already produced clear examples of intent-centric design.

Protocols like CoW Swap are a prime example. Instead of forcing users to manually find the best trading route through multiple liquidity pools (and risk front-running or MEV), CoW Swap lets users declare their intent: "I want to trade token X for token Y at the best possible price." Users sign this intent, and a competitive market of off-chain "solvers" competes to find the most efficient way to fulfill it. This abstracts away the complexity of execution and guarantees users a better, more protected outcome. This is a microcosm of the broader Intent Economy: users state their goal, and a competitive, decentralized network finds the optimal path to achieve it.

Conclusion

The Intent Economy represents a more equitable, efficient, and user-centric model for the internet. It promises a world with less friction, more creativity, and where technology is more aligned with human goals. The challenges are significant, but the opportunity is immense. The foundational layers are being built today, and the coming years will see a Cambrian explosion of innovation in this space.

Call for Builders

The Intent Economy is the next economic paradigm for the internet. The foundational layers are being built today.

If you are a founder, researcher, or engineer working on these foundational problems, we want to talk to you. Let's build an economy that is efficient, open and fair!

This article wouldn’t be possible without valuable input and comments from Yariv, Vangelis, Sam, Davide, Artem, Vitaly, Rico, Konstantin, Nikete, Mason, Lev.