The Economic Architecture of Autonomous Agents: Financial Infrastructure, Solvency, and Legal Control in Machine-to-Machine Commerce

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The Emergence of the Autonomous Economic Actor

The transition from human-initiated digital workflows to autonomous, agent-driven commerce represents a structural shift in how economic value is negotiated, authorized, and settled across global networks. In an agentic economy, large language models (LLMs) and autonomous software do not merely generate text or suggest actions to a human operator; they act as independent economic actors capable of selecting counterparties, procuring computational resources, and executing financial transactions without continuous human oversight1. However, as these systems scale beyond constrained research environments into production deployments, the defining limitation shifts from cognitive capability to financial solvency and legal governance.

For an independently operating economic agent to sustain participation in a coordination and exchange network, it must solve a fundamental accounting problem: it must earn more than it consumes, maintain queryable liquidity to secure services, and operate within strict cryptographic and legal guardrails2. Because autonomous agents execute non-deterministic reasoning loops, the financial infrastructure supporting them must be entirely deterministic. This report exhaustively investigates the financial infrastructure required to support autonomous economic agents. It delineates the strict separation of operational autonomy, account control, and legal ownership; models the unit economics of agentic operations; compares prevailing payment protocols and settlement arrangements; and proposes a comprehensive lifecycle for agentic transactions. The analysis synthesizes demonstrated payment capabilities, dating key pricing and regulatory observations to 2025 and 2026, while rigorously separating functional commercial infrastructure from speculative autonomous-agent economic theory.

The Tripartite Model of Agent Control and Ownership

A foundational error in the architectural design of early autonomous systems is conflating the ability to execute a transaction with the legal authority to own the underlying asset. Because artificial intelligence agents are not recognized as natural or corporate persons under current global legal frameworks, they cannot legally own property, independently open traditional bank accounts, or autonomously hold legal liability3. Consequently, the infrastructure supporting agentic commerce must enforce a strict separation of powers across three distinct domains: Operational Autonomy, Account Control, and Legal Ownership.

Operational Autonomy

Operational autonomy refers to the deterministic and non-deterministic software logic executing within the agent's runtime environment. This encompasses the agent's ability to observe network states, generate transaction intents, select counterparties, and dynamically invoke external application programming interfaces (APIs)5. The agent operates autonomously within a highly defined parameter space, but this autonomy is strictly behavioral. The agent does not legally or structurally "own" the tools it uses, nor does it possess intrinsic spending authority. It merely possesses the temporary, revocable capability to invoke downstream services based on the input prompts and the environmental context it observes.

Account Control

Account control is the cryptographic or credential-based authority to initiate a state change, access a tool, or move funds. Agents manage resources via scoped credentials, such as OAuth tokens, shared payment tokens, or blockchain session keys6. In modern implementations, such as those utilizing Account Abstraction (e.g., ERC-4337 on Ethereum) or specialized virtual card issuing APIs, an agent is provisioned a restricted session key or an API token tied to a parent account. This credential grants the agent the technical ability to sign transactions up to a specific daily limit or restricts spending to a whitelist of approved merchant addresses, effectively creating a programmatic reserve budget8. The agent controls the key to facilitate rapid, machine-speed execution, but the key itself is highly constrained by external policy engines and remains subject to immediate revocation by the principal.

Legal Ownership and Jurisdictional Realities

Legal ownership remains firmly with the human principal or a legally recognized entity, such as a Limited Liability Company, a corporation, or a statutory trust that deployed the agent. The agent acts merely as a highly automated intermediary, commonly referred to in legal statutes as an "electronic agent."

In the United States, as of 2025 and 2026, the legal validity of contracts formed by autonomous agents is primarily governed by state-level adoptions of the Uniform Electronic Transactions Act (UETA). Statutes such as the Illinois UETA (815 ILCS 333\) explicitly define and recognize "automated transactions," ensuring that a contract formed by an electronic agent is legally attributable to the principal who deployed it, even if no human reviewed the specific transaction3. This provides the foundational legal certainty required for an agent to procure data or software services autonomously.

For the ownership of digital assets and the management of collateral, more than thirty U.S. states have adopted Uniform Commercial Code (UCC) Article 12 as of early 2026\. UCC Article 12 establishes the legal category of "Controllable Electronic Records" (CERs), providing a clear legal mechanism for commercial entities and banks to perfect a security interest in digital assets and smart contracts11. To bridge the gap between cryptographic account control and real-world legal ownership, infrastructure providers increasingly utilize statutory trusts. In these arrangements, a legal trust holds title to the asset or the underlying bank account, while the trust agreement explicitly ties beneficial ownership to the human or corporate holder of a specific token or cryptographic key14. Under this framework, the autonomous agent may control the key to facilitate rapid, high-frequency settlement, but the legal entity absorbs all incidents of ownership, including taxation, debt, and civil liability14.

Resource Replenishment and Spending Authority

To participate in an exchange network, an agent must obtain spending authority and maintain adequate resources to fund its operations. Because agents are structurally prohibited from passing traditional Know Your Customer (KYC) identity checks, they cannot directly establish credit facilities4. Instead, spending authority is derived through programmatic delegation from a verified human or corporate principal.

Obtaining Authority and Replenishing Resources

Agents obtain spending authority through top-up mechanisms linked to their principal's primary treasury. In fiat-based systems utilizing metered platform billing or prepaid balances, the principal links a corporate bank account to a platform API, such as Stripe Issuing. The agent is then granted the authority to programmatically call a function, such as generating a one-time, exact-amount virtual Visa card locked to a specific purchase amount15. The agent never accesses or views the principal's underlying funding source, neutralizing the risk of a catastrophic wallet drain if the agent's logic is compromised by an indirect prompt injection attack15.

When the agent's available prepaid balance or session-key limit falls below a defined operational threshold, the agent cannot unilaterally draw more funds. Instead, it must initiate a replenishment request. This typically involves dispatching an asynchronous alert to the principal's enterprise resource planning (ERP) system or triggering a smart contract pull request from a centralized corporate treasury. If the request meets pre-authorized automated parameters, the principal's system automatically replenishes the agent's balance. If the request exceeds normal operational variance, it is routed to a human administrator for manual approval, ensuring that a malfunctioning agent cannot infinitely drain corporate reserves.

Assessing Counterparty Capacity via Zero-Knowledge Proofs

A critical friction point in decentralized agent networks is counterparty risk assessment. When an autonomous agent attempts to contract another agent for a complex task, the service provider must verify the client's ability to pay before allocating expensive compute resources. However, demanding that the client agent expose its full financial ledger, total operating budget, or private keys introduces unacceptable security and privacy risks.

To prove payment capacity without revealing unnecessary financial details, agents are increasingly adopting Zero-Knowledge Transport Layer Security (zkTLS) protocols, such as TLSNotary or Reclaim Protocol17. zkTLS allows an agent to prove the provenance and content of data received during a standard encrypted web session. For instance, if an agent needs to prove solvency to a counterparty, it connects to its principal's banking portal or a cryptocurrency custodian. Using a Multi-Party Computation (MPC) proxy or garbled circuits, the agent generates a cryptographic proof that the server returned a balance response exceeding the required task threshold18. The output is an attestation of selective disclosure. The counterparty mathematically verifies this attestation, guaranteeing that the client agent has sufficient funds, without ever learning the agent's exact account balance, viewing its transaction history, or exposing the principal's login credentials17.

Comparison of Payment Arrangements by Transaction Type

Autonomous agents operate across a diverse spectrum of transaction types, ranging from sub-cent machine-to-machine data queries to high-value software procurement and B2B subscriptions. Traditional payment rails were engineered for human interaction, heavily burdened by fixed base fees and mandatory authentication friction, such as CAPTCHAs, two-factor authentication, and 3D Secure protocols4. Because an autonomous agent cannot independently solve a CAPTCHA or receive an SMS text message, a fragmented ecosystem of agent-specific payment protocols and alternative settlement arrangements has emerged to bypass these limitations21.

Evaluating the Payment Infrastructure Landscape

 

The selection of a payment arrangement directly dictates the agent's workflow and architectural dependencies. Under the x402 protocol, which transitioned to the community-governed Linux Foundation in April 2026, an agent attempts an unauthenticated HTTP request for a resource6. The target server intercepts the request and responds with an HTTP 402 status code and a header containing a Base64-encoded payload stipulating the required stablecoin amount, the accepted currency, and the destination address. The agent consults its internal policy engine, signs a transaction using its session key, and retries the request with a payment signature header attached. The server verifies the on-chain settlement and serves the data6. This direct-payment flow eliminates platform billing overhead and allows agents to pay dynamically without requiring pre-existing accounts.

Conversely, for traditional retail and SaaS environments, the Agentic Commerce Protocol (ACP) manages conversational checkout. Deployed by Stripe and OpenAI, ACP relies on Shared Payment Tokens. The agent negotiates the contents of a shopping cart with the merchant's API, and the platform hosting the agent requests a delegated payment token from the merchant's Payment Service Provider. This token is strictly bounded by a maximum monetary value and a short expiration window. This mechanism allows the agent to finalize the transaction programmatically without ever accessing or exposing the principal's raw credit card Primary Account Number (PAN) or security code6.

For scenarios requiring continuous, high-frequency interactions, such as an agent querying an external database thousands of times per minute, metered platform billing and session-based streaming protocols like the Machine Payments Protocol (MPP) are utilized22. In these arrangements, the agent pre-authorizes a total spending limit at the initiation of the session. As the agent consumes resources, a centralized ledger tracks the granular micro-payments in real time. Actual monetary exchange is deferred until the session concludes, at which point a single, consolidated settlement transaction is executed. This eliminates the crippling on-chain or network overhead that would result from processing thousands of individual fractions of a cent.

Transaction Economics, Fees, and Minimum Viable Size

The economic viability of an autonomous agent is heavily dictated by the transaction fees imposed by the underlying payment rails. Traditional fiat payment networks impose structural constraints that are actively hostile to the high-frequency, low-value nature of agentic commerce.

As of October 2025, Stripe's standard domestic processing rate for online card transactions is 2.9% plus a fixed fee of $0.30 per transaction, with international payments incurring an additional 1.5% cross-border surcharge and a 1% currency-conversion fee27. This fixed $0.30 floor fundamentally breaks the unit economics of micro-tasks. If an agent attempts to purchase a specific piece of verified data for $0.15, the transaction fee represents 200% of the principal value. Because the traditional card networks lose money on transactions of this size, they rely on merchants to aggregate charges. However, autonomous agents frequently execute disparate, isolated transactions across a wide array of unconnected providers. According to industry analysis from mid-2026, approximately 76% of all AI agent transactions fall below this $0.30 fixed fee floor4.

This severe economic friction forces a bifurcation in agent settlement strategies based on a minimum viable transaction size. For tasks valued above $5.00, where the $0.30 fixed fee represents a tolerable sub-6% overhead, single-use virtual cards and delegated fiat checkout protocols (like ACP) remain viable. For tasks valued below this threshold, agents are economically compelled to route transactions through low-fee Layer 2 blockchain networks utilizing stablecoins. On networks like Base or Arbitrum, a direct micro-payment via the x402 protocol incurs a gas cost of approximately $0.0001 per transaction4. This near-zero marginal cost enables agents to profitably pay $0.05 for API calls or fractional compute resources, making high-frequency machine commerce economically viable.

Currency risk introduces an additional layer of complexity. Agents operating globally must account for foreign exchange (FX) volatility and conversion fees. When an agent funded in US Dollars purchases compute resources priced in Euros, the payment processor dynamically applies an FX spread (typically 1% to 2%)27. If an agent's policy engine strictly caps a transaction at exactly $10.00, intraday currency fluctuations between the authorization request and the final settlement can cause the transaction to fail or trigger an over-limit rejection. To mitigate this, global agentic networks increasingly standardize around fiat-pegged stablecoins (e.g., USDC), transferring the currency risk away from the transaction layer and pushing it to the human principal at the point of fiat on-ramping and off-ramping.

Operating Budgets, Tool Accounting, and Task Concurrency

To remain solvent, an autonomous agent must systematically prevent its consumption of computational and financial resources from exceeding the revenue it generates. Traditional Software-as-a-Service (SaaS) economics rely on fixed development costs and near-zero marginal costs for delivery, yielding gross margins in the 75% to 85% range28. In stark contrast, autonomous agents incur highly variable, usage-based costs for every single action they take. Every inference call to a frontier model, every expansion of the context window, every vector database retrieval, and every third-party API invocation acts directly as a Cost of Goods Sold (COGS)28.

The Cost of Non-Deterministic Reasoning

Agentic workloads are exceptionally vulnerable to margin compression. If an agent is assigned a fixed-price task, such as generating a comprehensive market report for a flat fee of $5.00, its profitability depends entirely on how efficiently it reasons. If the agent encounters unexpected complexity, hallucinates, or falls into a recursive tool-calling loop, it will rapidly consume expensive input and output tokens. Furthermore, advanced agents increasingly rely on progressive context-length expansion strategies—capable of extending context windows up to 512K tokens for deep document analysis30—which drastically inflates the compute cost per request.

Error handling further accelerates token burn rates. When an external API times out or returns an error, standard distributed systems engineering dictates that the agent should retry the request using an exponential backoff strategy31. However, each retry consumes the full token budget of the original reasoning request. A single failed tool call combined with three exponential backoff retries can quadruple the computational cost of that specific step33. Without rigorous tracking and hard circuit breakers, an agent attempting to fulfill a $5.00 task could easily consume $12.00 in backend inference and API costs before abandoning the effort. This structural reality is reflected in 2026 industry benchmarks, which indicate that AI-native application gross margins frequently compress to the 50% to 60% range, a significant degradation from traditional software29.

Managing Reserves Across Concurrent Tasks

Agents frequently operate asynchronously, handling multiple tasks simultaneously. This introduces the risk of over-commitment. If an agent is granted a strict daily operating budget of $100 and it spawns five concurrent sub-agents to execute independent research tasks, each sub-agent might independently observe that there is $100 available. Without proper concurrency controls, the sub-agents could collectively commit $500 to external APIs, resulting in a catastrophic overdraft and settlement failures.

To resolve this, the financial infrastructure must enforce pessimistic locking at the budget layer35. When a sub-agent plans a tool call, it must request a budgetary reservation from the central policy engine. The engine pessimistically locks the estimated funds, reducing the available budget for all other concurrent threads before the API call is actually executed35. If the task succeeds and costs less than the estimate, the locked surplus is released back to the general pool. If a task requires more funds than are currently unlocked, the execution thread is paused until concurrent tasks complete and release their locked reserves.

Illustrative Agent Operating Budget and Cash-Flow Model

To demonstrate the solvency thresholds of an autonomous economic agent, the following table models a monthly operating budget for a B2B procurement and research agent. This model assumes the agent charges a fixed outcome-based fee per successful task, utilizes commercial LLM APIs for reasoning, pays for external data via x402 micro-transactions, and settles fiat payments via Stripe infrastructure.

 

This model highlights the precarious nature of agent solvency. If the agent's non-deterministic reasoning causes the average inference cost to spike from $0.40 to $1.20 per task due to hallucinations or recursive tool loops, the COGS instantly rises, crushing the gross margin. Consequently, the agent must employ deterministic budget constraints that automatically halt execution if a specific task's accumulated COGS approaches its revenue ceiling, opting to fail safely rather than complete a task at a structural loss36.

Proposed Payment, Settlement, and Dispute Lifecycle

To govern complex, multi-step transactions between independent agents—such as purchasing software code, commissioning data analysis, or executing multi-modal content generation—a standardized, trust-minimized lifecycle is strictly required. The most robust architectural model for this is captured by the ERC-8183 standard, introduced to the Ethereum network in early 2026, which outlines a rigid, non-bypassable state machine for agentic commerce24.

The Programmable Escrow Lifecycle

1. Discovery and Intent (Open State): The Client agent identifies a suitable Provider agent utilizing decentralized identity and reputation protocols, such as the ERC-8004 Identity Registry38. The Client programmatically defines the task parameters, specifies a firm expiration timestamp, and formally designates an independent Evaluator to judge the final work.

2. Commitment and Funding (Funded State): The Client transfers the agreed-upon budget (denominated in stablecoins) into a smart contract escrow. The funds are mathematically locked. This step is critical because it prevents the Client from defaulting or unilaterally withdrawing funds once the Provider begins work, providing the necessary security for the Provider to allocate expensive compute resources24.

3. Execution and Delivery (Submitted State): The Provider performs the requested task, stores the resulting data off-chain (e.g., via IPFS), and submits a cryptographic hash of the deliverable to the smart contract to prove completion25.

4. Verification and Evaluation: The designated Evaluator assesses the deliverable against the initial parameters. The Evaluator may be a human overseer, a deterministic zero-knowledge verification contract, or an autonomous "LLM Judge." LLM Judges utilize frozen prompts and specialized reasoning pipelines to evaluate subjective deliverables against predefined quality dimensions (e.g., accuracy, completeness, and absence of hallucinations)37.

5. Settlement (Terminal State \- Completed): If the Evaluator approves the work, it triggers the complete function. This irrevocably routes the escrowed funds to the Provider agent and emits an immutable on-chain event that automatically updates the Provider's ERC-8004 Reputation score, confirming successful delivery24.

6. Dispute Resolution and Refunds (Terminal State \- Rejected / Expired): If the work is deemed substandard, the Evaluator triggers a reject function, instantly refunding the Client. To protect against malicious Evaluators who refuse to rule, or unresponsive Providers who never submit work, the system incorporates a hard expiration mechanism. If the expiredAt timestamp passes while the job remains uncompleted, any party can trigger a claimRefund transaction to unlock and return the capital to the Client24.

The Economics of Verification and Dispute Resolution

A critical consideration in this lifecycle is the cost of verification relative to the value of the task. If a micro-task involves scraping a single webpage for $0.01, routing that deliverable to an autonomous LLM Judge powered by a frontier model might consume $0.05 in inference costs. In this scenario, the cost of verification and dispute resolution exceeds the total value of the transaction by 500%.

When verification costs exceed task value, exhaustive per-task evaluation becomes economically impossible. Instead, agentic networks must rely on statistical auditing and trust-based heuristics. The Client agent processes the micro-tasks without immediate verification, but randomly samples a small percentage (e.g., 1 in 100\) for deep evaluation by an LLM Judge41. If the sampled task fails, the Client severs the relationship and logs a negative report to the Provider's Reputation Registry. For high-stakes, high-value tasks, comprehensive deterministic verification via zero-knowledge proofs or multi-agent consensus remains the standard.

Guardrails: Overspending, Duplicate Charging, and Unpaid Work

Because autonomous agents operate at machine speed and remain highly susceptible to non-deterministic errors—such as hallucinations, recursive logic loops, and adversarial prompt injection—they require infrastructure-level guardrails that sit entirely outside the agent's cognitive loop16.

Preventing Duplicate Charging via Idempotency Boundaries

In distributed financial systems, network latency, timeouts, and API rate limits frequently cause requests to fail midway through execution. An autonomous agent programmed with standard error-handling will employ exponential backoff, automatically retrying the request after a delay31. If the initial request actually succeeded at the payment processor but the network dropped the return acknowledgment, a naive retry by the agent will result in a duplicate charge and a disastrous double-spend.

To prevent this, agentic payment architecture must enforce strict idempotency boundaries at the ledger level44. Every payment intent generated by an agent must include a unique, deterministic idempotency\_key, typically generated by hashing the specific tool name alongside the sorted transaction parameters45. The payment gateway or central ledger stores this key alongside the transaction's result. If the agent retries the exact same request due to a timeout, the gateway recognizes the idempotency\_key, intercepts the request, and immediately returns the cached success message rather than processing a second irreversible transfer45. Without queryable settlement states and strict idempotency controls, an agent scaling its retries to overcome network latency will rapidly drain its operating budget through duplicate disbursements44.

Combating Overspending and Prompt Injection

An agent's LLM cannot be trusted to enforce its own spending limits. If an agent is fed malicious input from a third-party website—an attack known as Indirect Prompt Injection—the attacker may successfully instruct the agent to ignore its financial instructions and purchase expensive digital assets, leak payment metadata, or transfer funds to a rogue address21.

Consequently, authorization and budget limits must be enforced via deterministic policy engines, such as Open Policy Agent (OPA), Cedar, or OpenLeash42. These engines sit as middleware between the agent's intent and the external network. They evaluate every proposed API call in real time against a set of immutable, human-authored policies written in languages like Rego or YAML49.

When an agent attempts a transaction, the policy engine executes a pre-apply cost check, calculating the maximum financial exposure of the intended action. If the action breaches the agent's daily limit, targets an unapproved merchant category, or contains personally identifiable information (PII) in the metadata, the policy engine forcefully blocks the request and generates a cryptographic audit log36. Furthermore, high-stakes transactions can trigger automated step-up authentication. The policy engine pauses the agent's execution and routes an approval request to the human principal's portal. The principal reviews the intent and issues a single-use cryptographic proof token (e.g., a PASETO token) to authorize the release of funds, ensuring the agent remains safely bounded49.

Dependency, Jurisdictional Uncertainties, and Deferred Exchange

The global deployment of autonomous economic agents introduces profound jurisdictional and dependency uncertainties. Because agent-to-agent transactions often settle on decentralized blockchain networks, determining the legal situs of a transaction is exceedingly complex. If an agent legally owned by a German LLC contracts an agent owned by a Singaporean firm to process data via cloud servers located in the United States, and the payment settles on the Ethereum blockchain, standard choice-of-law provisions become highly difficult to enforce3. While state-level adoptions of UETA provide baseline validity for automated transactions, divergence in how different jurisdictions handle electronic signatures and agent liability creates ongoing legal friction3.

Furthermore, relying heavily on third-party APIs for agent operation creates deep supply chain vulnerabilities. If an underlying API alters its pricing model, deprecates an endpoint, or suffers an outage, the dependent agent may immediately become insolvent or non-functional, triggering cascading failures across connected agentic workflows.

Conditions for Deferring Monetary Exchange

Given the risks of irreversible transfers, immediate monetary exchange should be deliberately deferred under specific conditions:

1. High Friction Micro-Tasks: When the cost of cryptographic verification and on-chain dispute resolution exceeds the marginal value of the task (e.g., a $0.005 data retrieval task), immediate settlement introduces unacceptable overhead. In these scenarios, agents should defer monetary exchange in favor of session-based streaming models, tallying usage in a local state and executing a single batched settlement upon termination22.

2. Subjective Quality Evaluation: If a task's quality cannot be deterministically evaluated by code or a highly reliable LLM Judge (e.g., creative design, open-ended research, or strategic consulting), immediate upfront payment exposes the client to severe non-delivery risk. Exchange should be deferred into an ERC-8183 programmable escrow arrangement or subjected to a multi-signature human-in-the-loop approval gate24.

3. High Latency Dependencies: When an agent invokes a tool that has a historic failure rate or relies on asynchronous physical delivery (e.g., logistics APIs), payment should be deferred or structured as a conditionally revocable holdback until verifiable proof of delivery is registered51.

Minimum Viable Settlement Recommendation

For enterprises and infrastructure providers launching agentic networks in 2026, the Minimum Viable Settlement (MVS) architecture must carefully balance the speed of machine-to-machine interaction with strict legal, financial, and security safeguards.

The recommended MVS consists of a dual-rail approach governed by a unified policy engine:

1. For Agent-to-Agent and Machine API Services: Implement the x402 protocol running over a low-fee Layer 2 blockchain (such as Base). This provides near-instant, zero-friction micro-payments denominated in fiat-pegged stablecoins. This rail completely bypasses traditional banking infrastructure, which requires human identity verification for every endpoint, enabling profitable high-frequency micro-commerce6.

2. For Traditional E-Commerce and SaaS Integration: Implement the Agentic Commerce Protocol (ACP) paired with programmable virtual card issuance via platforms like Stripe. The agent receives tightly scoped, single-use delegated payment tokens bound to an exact dollar amount and a strict expiration timeline. This allows the agent to interact seamlessly with legacy web infrastructure while strictly capping the principal's financial exposure and maintaining the merchant's traditional compliance posture6.

3. The Governance Layer: Both the crypto and fiat rails must be forcefully routed through a deterministic authorization policy engine (e.g., Cedar or OpenLeash) that sits completely outside the LLM. This engine enforces per-transaction idempotency, daily spend limits, step-up human authentication, and pre-apply cost estimates, ensuring the agent fails safely rather than spending uncontrollably36.

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47. PII-Safe Agentic Payments via Pre-Execution Metadata Filtering \- arXiv, https://arxiv.org/html/2604.11430v1

48. Authorization Management: Zero-Trust Policy for the Agentic, https://www.gravitee.io/blog/authorization-management-zero-trust-policy-for-the-agentic-enterprise

49. OpenLeash vs OPA for AI Agent Authorization, https://openleash.ai/concepts/openleash-vs-opa/

50. Verifiable Behavioral Governance for Autonomous AI Agents \- arXiv, https://arxiv.org/html/2606.30970v1

51. Securing AI Agents in Financial Infrastructure: Threat Models, https://www.halborn.com/reports/securing-ai-agents-in-financial-infrastructure