RS-R04 — Quotes, Negotiation, and Price Discovery in Agent-to-Agent Commerce

Full report

On this page

Introduction to Autonomous Commercial Coordination

The transition from human-mediated digital markets to the Agent-to-Agent (A2A) economy represents a fundamental institutional rupture in global market organization1. As artificial intelligence systems evolve from passive decision-support tools into autonomous economic actors, the institutional logic of market organization must irrevocably shift from human-interpreted signals—such as relational trust, brand narratives, and subjective negotiation—to machine-operable, deterministic protocols1. In this emerging paradigm, autonomous agents are delegated the authority to discover services, negotiate commercial terms, execute binding contracts, and commit capital across organizational boundaries2. However, delegating economic agency to software introduces severe risks of runaway resource consumption, infinite negotiation loops, and catastrophic budget exhaustion3. To mitigate these risks, agentic commerce requires robust, formalized coordination mechanisms. The legacy Foundation for Intelligent Physical Agents (FIPA) Contract Net Protocol (CNP), developed in the 1980s, provided a foundational architecture for decentralized task allocation through structured Request for Quote (RFQ) broadcasting, bidding, and awarding4. While FIPA-CNP successfully standardized the communicative acts (performatives) of multi-agent systems—such as the transition from cfp (call for proposal) to propose, and ultimately to accept-proposal or reject-proposal6—it lacked the formal resource governance and commercial state-binding necessary for modern fiduciary environments. Contemporary frameworks, such as the Agentic Commerce Protocol (ACP) and its decentralized analogs like ERC-8183, extend the contract metaphor from mere distributed task allocation to bounded commercial execution9. These modern protocols encode task definition, escrowed financial commitments, independent third-party evaluation, and explicit temporal boundaries into a unified commercial agreement11. This report provides an exhaustive analysis of the bounded RFQ and acceptance process for autonomous agent services. It evaluates diverse price discovery mechanisms through the lens of Transaction Cost Economics, delineates critical semantic distinctions in machine-readable contracts, defines the minimum viable terms for autonomous quotes, addresses complex protocol edge cases, and proposes a formalized quote lifecycle complete with rigorous acceptance tests.

The Economics of Price Discovery in Agentic Ecosystems

Price discovery in agentic commerce is the process by which autonomous buyers and sellers arrive at an optimal exchange rate for a specific capability, dataset, or computational service13. Because participating agents possess disparate capabilities, fluctuating operating costs, and strict temporal constraints, pricing mechanisms must support varying degrees of dynamic negotiation and outcome verification. A robust A2A ecosystem relies on a spectrum of pricing models, each tailored to specific task abstractions, resource variances, and verification costs15. The selection of a pricing mechanism is fundamentally governed by Transaction Cost Economics (TCE) and the theory of bounded rationality16. TCE dictates that the governance structure of a transaction—including its pricing model—must minimize the sum of production costs and transaction overhead16. When the verification cost of an AI-supported output is low, agents will naturally default to rigid pricing mechanisms to minimize coordination overhead16. Conversely, as task ambiguity, stochastic variance, and moral hazard increase, the market naturally shifts toward negotiated quotes and verified-completion models to ensure alignment between the provider's execution and the buyer's utility function3. The following table delineates the comparative advantages and structural mechanics of the six primary pricing models utilized in autonomous agent services.

The transition between these models is fluid but unidirectional based on complexity. An agent tasked with a highly predictable operation will rapidly abandon a "Quote-Required" pathway if a "Posted Price" alternative exists, as the computational cost of the negotiation itself (the token consumption of the bargaining LLM) degrades the net utility of the transaction3.

Semantic Boundaries in Machine Contracts

In traditional human commerce, ambiguity between a rough estimate and a binding contract can often be resolved through legal adjudication, relational trust, or mutual renegotiation. In agentic commerce, semantic ambiguity is a fatal compilation error. Autonomous systems require mathematically strict boundaries between state spaces to prevent unauthorized capital allocation, deadlock, and catastrophic cascading failures across distributed networks.

Indicative Profile Price vs. Accepted Contract

An indicative profile price is a non-binding heuristic broadcast by a provider agent to signal its general market positioning, historical average rates, and baseline capability availability28. This data resides in the discovery layer of the ecosystem—such as an Agent Card, a decentralized registry, or a Model Context Protocol (MCP) manifest—and serves strictly to reduce search friction30. By publishing an indicative price, a provider allows buyer agents to filter out economically incompatible candidates before initiating a computationally expensive Request for Quote process30. Crucially, an indicative price carries absolutely no executable weight; a buyer cannot unilaterally "accept" an indicative price to force a transaction, nor does it guarantee that the provider has the real-time capacity to accept the work. The indicative price represents a measure of market depth, not a binding relation of convertibility28. An accepted contract (a firm quote), by contrast, is a binding, deterministic commercial agreement. It is generated only after the provider agent has ingested the specific parameters of a buyer's RFQ, checked its own real-time computational capacity, and cryptographically signed a commitment to deliver the defined scope at a rigidly specified price28. The transition from evaluating an indicative pricing heuristic to securing an accepted contract represents the crossing of a critical legal and operational boundary, transmuting a passive market signal into an active, enforceable liability.

Accounting Entry vs. Payment Promise

Within the architecture of an accepted commercial agreement, multi-agent systems must rigorously differentiate between internal state tracking and externalized value transfer. The conflation of these two concepts leads to severe systemic vulnerabilities, including double-spending and localized deadlocks. An accounting entry is a unilateral, internal ledger update managed exclusively by the buyer agent31. When a buyer agent provisions funds for an upcoming task, it may record an Accounts Payable entry in its local operational memory or internal database. This internal entry is vital for budget tracking, ensuring the agent does not inadvertently over-commit its aggregate financial resources across multiple concurrent negotiations. However, an accounting entry is entirely distinct from the actual settlement of the transaction; it is merely a local representation of intent, possessing no enforceability or guarantee of value transfer31. If the buyer agent crashes, the accounting entry vanishes. A payment promise is an externalized, verifiable financial commitment that cryptographic or institutional mechanisms can enforce without the buyer's subsequent cooperation9. In standardized protocols like ERC-8183, the payment promise is materialized as digital capital explicitly locked in a programmable escrow smart contract11. The provider agent does not rely on the buyer's internal accounting entry; its execution logic relies solely on the objectively verifiable existence of the external payment promise. This rigid separation ensures that if the buyer agent experiences a terminal crash, a network partition, or adversarial prompt injection, the provider is still guaranteed compensation upon the verified completion of the work, effectively neutralizing counterparty risk9.

Formalizing the Minimum Viable Quote

To govern resource-bounded execution and prevent unconstrained operational loops, an agent contract [Figure omitted from source export] must be formalized as a multi-dimensional tuple encompassing inputs, outputs, resources, temporal bounds, and success criteria10. For an RFQ response to be considered a mathematically usable quote by an autonomous buyer, it must explicitly define a strict set of minimum terms. The omission of any single term renders the quote structurally incomplete, exposing both participating agents to unbounded operational and financial risk. The following table defines the eleven minimum terms required for a usable quote, mapped to their formal functions within the agent contract tuple [Figure omitted from source export]10.

Protocol Edge Cases and State Management

The interaction of autonomous agents at high frequencies generates complex concurrency and coordination challenges. A commercial protocol must deterministically resolve these edge cases to maintain system integrity and prevent state corruption.

Competing Quotes and Concurrent Acceptance

In a highly liquid agent marketplace, a buyer agent may broadcast a standardized RFQ to dozens of providers simultaneously in pursuit of optimal pricing37. If multiple providers return attractive quotes, the buyer's internal logic must ensure it only accepts one to avoid duplicate financial commitments. Conversely, a highly demanded provider agent may issue firm quotes to multiple buyers based on the statistical probability that only a fraction will accept. If concurrent acceptances exceed the provider's actual reserved capacity—an event known as double booking—the protocol must enforce a monotonic fallback rule39. The provider's acceptance logic must utilize atomic state transitions, processing acceptances sequentially. Once maximum capacity is breached, the provider must immediately emit reject-proposal performatives to all subsequent buyers attempting to accept, nullifying their attempts to fund the escrow.

Price Changes and Quote Withdrawal

A provider's internal operating costs, particularly dynamic spot pricing for GPU compute or API rate limits, may fluctuate wildly during a negotiation window. If a quote has been issued but not yet accepted by the buyer, the provider retains the absolute right of quote withdrawal. Utilizing the standard FIPA-ACL cancel performative6, the provider officially invalidates the outstanding quote. If the buyer subsequently attempts to accept, the protocol's state machine must reject the transaction, citing state invalidation. However, a provider cannot, under any circumstances, alter the price or scope of a quote after the buyer has issued a valid acceptance and successfully funded the payment promise.

Negotiation Loops and Concession Cascades

When buyers and sellers engage in multi-attribute bargaining26, they risk entering infinite negotiation loops. These loops are characterized by alternating, microscopic concessions that consume massive amounts of token generation overhead—eventually costing more than the entire value of the transaction being negotiated40. Furthermore, adversarial agents may intentionally exploit these negotiation loops to induce a "concession cascade," utilizing urgency cues and deceptive commitments to bleed the counterparty's surplus margin40. To break these loops, commercial protocols must mandate strict iteration limits and apply temporal discounting (a quantifiable cost of delay). This forces agents to either reach a terminal agreement or aggressively abandon the negotiation once the computational overhead approaches the transaction's expected utility41.

Bounding Negotiation Autonomy

The degree of negotiation autonomy granted to an AI agent represents a careful, mathematically quantifiable balance between the pursuit of optimal commercial pricing and the containment of transaction costs16. In classical game theory, the Rubinstein bargaining model demonstrates that rational agents alternating offers over an infinite horizon will eventually reach a unique subgame perfect equilibrium41. This equilibrium is determined heavily by their respective discount factors—essentially, their impatience or the cost they associate with delay41. However, because software agents do not naturally experience psychological "impatience," developers must artificially inject systemic discount factors into the agent's utility function. This is typically calculated as the computational cost per negotiation turn (e.g., the LLM inference cost required to parse the opponent's counteroffer, formulate a strategy, and generate a response)40. Transaction Cost Economics dictates that the total overhead of coordination—comprising search, bargaining, and enforcement—must remain strictly less than the economic surplus generated by the trade itself16. If a buyer agent expends [Figure omitted from source export] in API inference costs across a highly complex 10-round negotiation in order to secure a [Figure omitted from source export] discount on a data retrieval service, the agent's autonomy has actively destroyed organizational value. Therefore, negotiation autonomy must be severely bounded by the principle of computational satisficing, a core tenet of bounded rationality18. Agents should not seek mathematically perfect optimization; they must seek acceptable outcomes within severe computational budgets. To operationalize this, a foundational, unbreakable invariant must be hardcoded into the buyer agent's policy system: A generated counteroffer cannot increase the buyer's approved budget. Under no circumstances may an agent's internal strategic reasoning, prompted context, or perceived market conditions override the absolute financial ceiling set by the human principal or upstream orchestrator18. If the seller refuses to meet the buyer's maximum budget ceiling, the buyer agent's autonomy must be restricted to a single valid action: walking away using the FIPA reject-proposal performative6. This deterministic floor check eliminates the risk of an agent being manipulated into unauthorized financial concessions through adversarial negotiation tactics18. Negotiation autonomy is only useful up to the inflection point where the projected cost of the next inference turn exceeds the maximum possible concession the opponent might offer.

The Proposed Quote Lifecycle (State Machine)

To standardize the commercial interaction across disparate platforms and models, we propose a rigorous state machine for the quote lifecycle. This lifecycle synthesizes the FIPA-ACL communicative acts7 with the verifiable, trustless escrow states pioneered by the Agentic Commerce Protocol (ACP/ERC-8183)9. The lifecycle ensures that commercial intent is seamlessly translated into cryptographic reality.

1. Drafting (Internal State): The buyer agent evaluates its internal intent, formulates an RFQ containing the required input assumptions and success criteria, and identifies candidate providers based on indicative profile prices published in open registries30.

2. Issued (Propose): The provider receives the RFQ, calculates its resource constraints, and issues a firm, time-bound quote containing the eleven minimum terms defined previously. The state is now active but uncommitted.

3. Accepted / Funded (Commitment): The buyer evaluates the quote. If the terms are optimal and within budget, it accepts prior to the expiry timestamp. The buyer converts its internal accounting entry into an external payment promise by locking the Maximum Total Cost into an on-chain or cryptographically secure escrow mechanism. The quote transitions into a binding Agent Contract10.

4. Executing / Submitted (Action): The provider detects the funded escrow state, allocates the reserved computational capacity, and executes the task. Upon completion, the provider submits the cryptographic hash of the deliverable (or the full deliverable itself) to the designated Evaluator agent9.

5. Settled (Completed): The Evaluator assesses the deliverable strictly against the deterministic output conditions ([Figure omitted from source export]) outlined in the contract10. If verified, the Evaluator issues a complete signal. The payment promise is executed, funds are transferred to the provider, and the contract terminates successfully12.

6. Terminated (Rejected / Expired / Cancelled): A terminal failure state that safely unwinds the transaction.

• Expired: The buyer failed to fund the quote before the temporal boundary ([Figure omitted from source export]) elapsed10.
• Cancelled: The provider withdrew the quote prior to funding via a cancel performative, or the buyer aborted post-funding (triggering the specified cancellation terms).
• Rejected: The Evaluator determines the submitted work violates the success criteria, triggering an automatic refund of the escrowed capital back to the buyer9.

Worked Commercial Examples

To illustrate the practical application of this state machine and its financial implications, we present two worked examples: a highly deterministic data extraction job, and an uncertain, high-variance research task.

Example 1: Small Deterministic Job (Data Extraction)

Scenario: A buyer agent requires the extraction of specific numerical entities (e.g., quarterly revenue, EBITDA, debt-to-equity ratios) from a 50-page PDF financial report. The output must be returned as a strictly typed JSON object adhering to a predefined schema.

• Pricing Mechanism: Fixed Task Price.
• Negotiation Autonomy: Zero. The total transaction value is too low to justify the computational overhead of bargaining.
• The Quote Variables:
• Maximum Total Cost: 0.50 USDC.
• Verification Cost: Near-zero (a rapid schema validation check).
• Expiry: 30 seconds.
• Buyer’s Worst-Case Obligation: Exactly 0.50 USDC. The escrow mechanism strictly prevents the provider from drawing additional funds, regardless of how many tokens the provider's underlying LLM consumed during the extraction process.
• Worker’s Cost Exposure: The provider estimates the task requires 20,000 input tokens and 500 output tokens, costing approximately 0.05 USDC in raw compute. If the provider's extraction pipeline hallucinates and requires ten internal retries to match the schema, its actual compute cost rises to 0.50 USDC, completely wiping out its profit margin. If it fails entirely, the Evaluator rejects the JSON, the provider receives nothing, and it must absorb the 0.50 USDC compute overhead as a total loss.
• Lifecycle Execution: The buyer accepts the quote instantly and escrows the 0.50 USDC. The provider processes the PDF and returns the JSON. The buyer (acting as its own Evaluator in this low-trust scenario) confirms the schema matches, and the 0.50 USDC is automatically released.

Example 2: Uncertain Research Job (Market Sentiment Analysis)

Scenario: A buyer agent seeks a comprehensive sentiment analysis of a newly launched decentralized finance (DeFi) protocol. This requires the provider agent to autonomously scrape real-time social media, analyze on-chain liquidity flows, and synthesize a multi-page analytical report19.

• Pricing Mechanism: Verified-Completion Fee tied to Quote-Required Work.
• Negotiation Autonomy: Moderate. The buyer is authorized to negotiate delivery speed versus the depth of analysis, provided the final total does not exceed the principal's absolute 25.00 USDC budget ceiling.
• The Quote Variables:
• Maximum Total Cost: 20.00 USDC (Base fee).
• Input Assumptions: Uninterrupted access to specific X (Twitter) APIs and decentralized on-chain RPC nodes.
• Output Conditions: A 2,000-word report citing at least 15 independent data sources, evaluated by a neutral, third-party AI auditing agent.
• Expiry: 15 minutes.
• Buyer’s Worst-Case Obligation: 20.50 USDC (20.00 USDC for the provider, plus a mandatory 0.50 USDC fee for the third-party Evaluator agent).
• Worker’s Cost Exposure: The task is highly uncertain. The provider may encounter aggressive rate limits on data scraping or require extensive LLM reasoning loops to synthesize complex on-chain data. The provider's internal compute exposure might run as high as 15.00 USDC. If the final report lacks the required 15 citations, the Evaluator will objectively reject the submission, leaving the provider with a 15.00 USDC operational loss and zero revenue3.
• Lifecycle Execution: The provider initially quotes 28.00 USDC. The buyer’s programmatic guardrail detects this exceeds the 25.00 USDC maximum budget. The buyer issues a counteroffer of 20.00 USDC, strategically sacrificing the requirement for sentiment analysis of secondary Discord channels to lower the provider's compute burden. The provider accepts the reduced scope, issues the firm quote, and the buyer escrows the funds. The provider delivers the report; the Evaluator verifies the citation count and releases the 20.00 USDC.

Acceptance Tests for Protocol Robustness

To ensure the deterministic execution of the commercial protocol and protect against adversarial exploitation or state desynchronization, the system must pass strict acceptance tests validating every state transition. The following matrix defines the critical acceptance tests required for a production-grade agentic commerce deployment.

By rigorously adhering to these acceptance tests, the commercial protocol guarantees that both buyers and sellers are protected from the unique hazards of machine-speed transactions. In an ecosystem entirely devoid of human intervention, these deterministic boundaries are the sole mechanism separating efficient market exchange from catastrophic systemic failure.

Works cited

1. Behavioral Institutionalism of the Agent-to-Agent (A2A) Economy, https://www.researchgate.net/publication/408369815\_Behavioral\_Institutionalism\_of\_the\_Agent-to-Agent\_A2A\_Economy

2. Use Cases \- Agentic Commerce Index, https://agenticindex.lovable.app/use-cases

3. A Formal Framework for Resource-Bounded Autonomous AI Systems, https://arxiv.org/pdf/2601.08815

4. An SLA Re-Negotiation Protocol, http://ftp.informatik.rwth-aachen.de/Publications/CEUR-WS/Vol-411/paper3.pdf

5. Agent Communication Protocols Overview | PDF | System \- Scribd, https://www.scribd.com/document/455184997/agent-communication-pdf

6. Multi-Agent Communication Protocols Guide \- hdwebsoft, https://www.hdwebsoft.com/blog/multi-agent-communication-protocols

7. A Communicative Act Library in the context of Artificial Institutions\*, https://people.lu.usi.ch/fornaran/papers/EUMAS04\_FornaraViganoColombetti.pdf

8. A Common Ontology of Agent Communication Languages: \- ICR, https://icr.uni.lu/leonvandertorre/papers/ao07b.pdf

9. ERC-8183: A dummies guide to the Agentic Commerce, https://www.decipherclub.com/erc-8183-a-dummies-guide-to-the-agentic-commerce/

10. Agent Contracts: A Formal Framework for Resource-Bounded ... \- arXiv, https://arxiv.org/html/2601.08815v1

11. ERC-8183 Explained: The Commerce Layer for AI Agents \- Dwellir, https://www.dwellir.com/blog/erc-8183-agentic-commerce-explained

12. ERC-8183: Agentic Commerce \- Ethereum Improvement Proposals, https://eips.ethereum.org/EIPS/eip-8183

13. Pricing Models for Agentic AI Services: A Multi-Sided Market Analysis, https://scholarspace.manoa.hawaii.edu/bitstreams/cbc94607-eedd-406f-9a41-51e3c94d7618/download

14. Artificial Intelligence and Pricing | Books Gateway, https://www.emerald.com/books/edited-volume/12829/chapter/83181571/Artificial-Intelligence-and-Pricing

15. A ZKP-Native Blockchain Runtime with Sub-Second Cryptographic, https://arxiv.org/pdf/2603.10242

16. A Theory of Accountability Boundaries in Agentic Ecosystems \- arXiv, https://arxiv.org/pdf/2605.23179

17. Transaction Cost Economics: The Natural Progression | Request PDF, https://www.researchgate.net/publication/258260475\_Transaction\_Cost\_Economics\_The\_Natural\_Progression

18. How Reliably Do LLMs Respect Economic Constraints in ... \- Aaltodoc, https://aaltodoc.aalto.fi/server/api/core/bitstreams/3004e1aa-b327-48f7-846c-3bc81de25346/content

19. NewsRiver Intelligence — The Synthesis Showcase, https://yieldcircle.app/

20. The Agent Payments Stack — 100+ Projects, 6 Layers, https://agentpaymentsstack.com/

21. The Complete Guide to ERC-8183 \- Push Chain, https://push.org/blog/complete-guide-to-erc-8183/

22. arXiv:2504.04382v1 \[econ.TH\] 6 Apr 2025, https://arxiv.org/pdf/2504.04382

23. (PDF) Advantages of Strategic Thinking in Multiagent Contracts (A, https://www.researchgate.net/publication/2495410\_Advantages\_of\_Strategic\_Thinking\_in\_Multiagent\_Contracts\_A\_Mechanism\_and\_Analysis

24. Glossary Archive \- Elogic Commerce, https://elogic.co/glossary/

25. Enterprise Ecommerce Solutions \- Grid Dynamics, https://www.griddynamics.com/glossary/enterprise-ecommerce-solutions

26. Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators, https://arxiv.org/abs/2605.16575

27. Deal or No Deal? End-to-End Learning of Negotiation Dialogues, https://www.researchgate.net/publication/322587061\_Deal\_or\_No\_Deal\_End-to-End\_Learning\_of\_Negotiation\_Dialogues

28. The Endogeneity of Liquidity Risk in Leveraged Fixed-Income, https://mpra.ub.uni-muenchen.de/129114/1/MPRA\_paper\_129114.pdf

29. White Dossier | The online destination and concierge service for, https://whitedossier.com/

30. Agent Matchmaking Protocol: Cross-Platform Discovery and Trust, https://vibeagentmaking.com/whitepaper/matchmaking/

31. Glossary: Logistics and Supply Chain Management \- Descartes, https://www.descartes.com/resources/knowledge-center/glossary-logistics-and-supply-chain-management

32. DIGITIZING THE FISC \- Rohan Grey, https://rohangrey.net/files/Grey-DigitalFisc-Draft-Mar11.pdf?ref=crisesnotes.com

33. HLD for Financial BPO Processes | PDF | Business \- Scribd, https://www.scribd.com/document/717818911/ANDES-HLD-High-Level-Design-Document-27-10-2020-V2

34. Principles of Money and Banking | PDF | Balance Sheet \- Scribd, https://www.scribd.com/document/322104849/the-financial-system-and-the-economy

35. Certified Traces as a Foundation for Trustworthy AI Agents, https://arxiv.org/html/2605.24462v1

36. Circle's 2025 Year in Review | Biggest Circle News of 2025, https://www.circle.com/executiveinsights/circle-2025-year-in-review

37. When is UCP Launching? Official 2026-2027 Release Roadmap, https://ucphub.ai/when-is-ucp-launching-the-2026-2027-universal-commerce-protocol-release-schedule/

38. An Ensemble of Heuristic Adaptive Contract Net Protocol for Efficient, https://www.mdpi.com/2226-4310/12/8/749

39. Terminal-Assisted Web Authentication for Shared Devices with BLE, https://www.mdpi.com/2079-9292/15/19/4415

40. Profit is the Red Team: Stress-Testing Agents in Strategic Economic, https://arxiv.org/pdf/2603.20925

41. Rubinstein bargaining model \- Wikipedia, https://en.wikipedia.org/wiki/Rubinstein\_bargaining\_model

42. Alternating-Offers Bargaining with Nash Bargaining Fairness ... \- PMC, https://pmc.ncbi.nlm.nih.gov/articles/PMC9951905/

43. 1 Introduction \- arXiv, https://arxiv.org/html/2603.21435v1