AGENT INTENT OPTIMIZATION: A CONCEPTUAL FRAMEWORK FOR MARKETING IN THE AGE OF AUTONOMOUS AI SHOPPERS
Paul F. Accornero
Affiliations
Doctoral Candidate (PhD by Publication) [PRIMARY]
Founder, The AI Praxis
SSRN Working Paper Series
Uploaded October 2025 (Article drafted April 2025)
PRELIMINARY DRAFT - PLEASE DO NOT CITE WITHOUT PERMISSION
Comments welcome: paul.accornero@gmail.com
Tel: +39 3351241888
ORCID: https://orcid.org/0009-0009-2567-5155
ABSTRACT
Autonomous AI agents functioning as consumer intermediaries—“synthetic shoppers”—fundamentally restructure marketing strategy. This paper introduces Agent Intent Optimization (AIO) as a strategic marketing discipline for influencing autonomous purchase decisions, distinguishing it from Search Engine Optimization (SEO) and emergent Generative Engine Optimization (GEO).
Drawing on search theory, information economics, and platform strategy, we develop the Agent Decision Preference Stack—a three-layer framework explaining how autonomous agents organize purchase decisions: foundational directives (hard constraints), inferred knowledge (learned heuristics), and real-time optimization (operational factors). The framework predicts that operational excellence will increasingly trump traditional brand equity in agent-mediated commerce, as agents prioritize verifiable, current-state information over historical reputation signals.
We articulate three strategic imperatives (algorithmic visibility, persuasion, and relationship-building) supported by fourteen testable propositions. Illustrative computational simulation and case study analysis provide proof-of-concept evidence for the framework’s validity, demonstrating that operational factors exert substantially stronger influence than reputation factors on agent selection.
This conceptual framework advances marketing theory for algorithmic intermediation while opening avenues for comprehensive empirical investigation. Practical implications require expanding marketing’s domain beyond persuasion to encompass operational capabilities that agents evaluate.
Keywords: agent intent optimization, AI agents, algorithmic marketing, marketing strategy, theoretical framework
Note: This is a working paper presenting a conceptual framework with illustrative validation. Comprehensive empirical validation is forthcoming.
AUTHOR BACKGROUND NOTE
The author’s 25+ years of senior commercial leadership in global consumer goods informs the practical insights in this research, conducted independently as part of doctoral work. Theoretical frameworks are developed through academic research and will be elaborated in a forthcoming practitioner-oriented book (Macmillan Publishers, 2026-2027).
RESEARCH TRANSPARENCY & OPEN SCIENCE
This research adheres to open science principles:
All simulation code, agent decision models, and prompts available upon request
Full transparency in AI tool usage (detailed below)
Reproducible methodology with documented parameters
All 30 case study scenarios provided in supplementary materials
Single-researcher design with explicit limitations acknowledged
Generative AI Tools Disclosure
This research employs generative AI in three distinct ways:
1. Research Tools (Editorial Assistance): - Claude (Anthropic): Literature synthesis, reference management, coding assistance - Google Gemini: Background research only
2. Research Subjects (Empirical Data): - Claude, ChatGPT, Perplexity: Case study participants (N=30 scenarios) - Synthetic agent simulation: Computational validation (N=600 decisions)
All intellectual contributions—theoretical framework, strategic imperatives, empirical design, interpretations, and conclusions—are the author’s original work. The author accepts full responsibility for all content and scholarly integrity.
CONFLICT OF INTEREST STATEMENT
The author’s employment at De’Longhi Group did not influence this research. No funding, resources, or review was provided by De’Longhi or any commercial entity. This work represents independent scholarly contribution to marketing literature.
INTELLECTUAL PROPERTY & INDEPENDENCE STATEMENT
Independent Academic Work: This research represents independent scholarly work conducted solely in the author's capacity as a doctoral candidate, not as an employee of De'Longhi Group or any other organization.
No Employment Connection:
All research activities were conducted:
- Entirely outside regular employment hours
- Without use of employer facilities, equipment, data, or resources
- Without employer funding, support, or involvement
- Using only the author's personal resources and publicly available information
- Without disclosure of confidential or proprietary employer information
Sole Ownership: The author retains sole and exclusive ownership of all intellectual property in this work, including but not limited to: the Agent Intent Optimization framework, the Agent Decision Preference Stack, all theoretical models, propositions, methodologies, and terminology introduced herein.
No Endorsement: This work does not represent the views, positions, strategies, or endorsements of De'Longhi Group or any other organization.
General Industry Experience: Practical insights are derived from the author's 25+ years of general industry experience across multiple organizations and publicly available market knowledge, not from confidential information of any specific organization.
Copyright Notice: Copyright © 2025 Paul F. Accornero. All rights reserved. No part of this work may be reproduced or used for commercial purposes without express written permission from the author.
Academic Use: This work is intended solely for academic and educational purposes.
FUNDING
None
1. INTRODUCTION
Marketing has undergone repeated transformations as new intermediaries enter the exchange process between firms and consumers. Each intermediary—from department stores to television to digital platforms—has required novel strategic capabilities (Lamberg & Tikkanen, 2006; Ringberg, Reihlen, & Rydén, 2019). We now face perhaps the most profound shift: autonomous artificial intelligence agents functioning as “synthetic shoppers”—algorithmically-constructed consumer proxies that discover, evaluate, and purchase products with substantial autonomy.
Recent technological advances enable AI agents to function as sophisticated shopping intermediaries acting with minimal human supervision (Davenport, Guha, Grewal, & Bressgott, 2020; Huang & Rust, 2021). When a consumer instructs an agent to “buy the most environmentally sustainable coffee that fits my budget and preferences,” the agent independently researches certifications, compares environmental data, analyzes reviews, and completes purchase—all without presenting intermediate options. This represents a fundamental shift from assistive tools to autonomous decision-makers: the “Shopper Schism,” the permanent separation of the human consumer who uses a product from the synthetic shopper that purchases it (Accornero, forthcoming 2027).
This transformation poses an existential challenge for marketing scholarship. Marketing theory has been built on understanding human psychology, cognition, and behavior. When autonomous agents mediate this relationship, standing between marketer and consumer, foundational assumptions require reconsideration.
Parallel developments in AI interaction have generated practitioner interest in Generative Engine Optimization (GEO)—ensuring brand content appears in AI-generated responses to user queries (Aggarwal et al., 2024; Walker Sands, 2025). GEO addresses scenarios where consumers use large language models (ChatGPT, Gemini, Perplexity) to research topics and compare options. Here, agents synthesize knowledge and recommend; consumers retain decision authority. GEO makes your brand the authority an informational AI quotes; Agent Intent Optimization (AIO) makes your product the verifiable choice a transactional AI buys.
This distinction is critical. When agents answer questions, they synthesize and recommend; when agents execute purchases, they decide and transact. The former influences consideration; the latter determines commerce. While many agents operate across both dimensions, our focus on transactional optimization reflects its greater commercial consequence and relative scholarly neglect.
This working paper introduces the conceptual framework for Agent Intent Optimization. While we provide illustrative validation demonstrating the framework’s computational feasibility and descriptive accuracy, comprehensive empirical validation represents an extensive research program extending beyond this initial conceptual contribution. We position this work as establishing theoretical foundation and inviting scholarly engagement in testing the framework’s predictions across diverse contexts.
We define AIO as the systematic practice of optimizing a firm’s digital presence, product information, and operational infrastructure to influence autonomous AI agents’ product selection and transaction execution on behalf of their human principals. Through conceptual analysis grounded in search theory, information economics, and platform strategy, we develop a framework distinguishing AIO from SEO and GEO.
Our contribution is threefold. First, we provide theoretical grounding for AIO by synthesizing insights from search theory (Stigler, 1961; Nelson, 1970), information economics (Akerlof, 1970; Spence, 1973), platform theory (Rochet & Tirole, 2003), and digital marketing scholarship, explicitly positioning AIO relative to informational optimization (GEO). Second, we develop the Agent Decision Preference Stack—an integrative framework positioning informational and transactional optimization within a unified theory of how autonomous agents prioritize decisions across three layers: foundational directives, inferred knowledge, and real-time optimization. Third, we articulate three core strategic imperatives for AIO success supported by fourteen testable propositions.
The remainder proceeds as follows. Section 2 reviews relevant literature on search behavior, digital marketing, and algorithmic intermediation. Section 3 develops our conceptual framework, distinguishing AIO from SEO and GEO. Section 4 articulates three strategic imperatives of AIO. Section 5 introduces the Agent Decision Preference Stack. Section 6 provides proof-of-concept validation. Section 7 discusses implications. We conclude with limitations and research directions.
2. THEORETICAL BACKGROUND AND LITERATURE REVIEW
2.1 Search Theory and Information Economics
Search theory examines how buyers and sellers overcome information asymmetries. Stigler’s (1961) work established that search is costly and rational actors search only until marginal cost equals expected marginal benefit. Nelson’s (1970) distinction between search goods (attributes verifiable before purchase), experience goods (quality revealed through use), and credence goods (attributes not easily verified even after consumption) provides essential groundwork for understanding agent-mediated commerce.
AI agents fundamentally alter this taxonomy. What were experience or credence goods may become search goods when agents access and aggregate post-purchase reviews, laboratory data, or certifications at scale. An agent evaluating coffee quality analyzes aggregated reviews from thousands of consumers with similar documented preferences, transforming an experience good into a data-analyzable search good.
Information economics further illuminates agent-mediated markets. Akerlof’s (1970) analysis of information asymmetry applies directly to agent optimization. When agents possess superior information-processing capabilities, they can overcome information asymmetries historically plaguing markets. However, new asymmetries emerge: between firms providing machine-readable data versus those that do not; between firms with API-accessible information versus static web content; between firms earning agent trust through consistent performance versus those unable to demonstrate reliability algorithmically.
Spence’s (1973, 2002) signaling theory becomes particularly relevant. In human-mediated markets, firms signal quality through advertising expenditure, premium packaging, celebrity endorsements. Agents cannot process these signals. Instead, they evaluate third-party certifications with blockchain verification, API-accessible supply chain transparency, real-time inventory accuracy, and structured product specifications. The cost of signaling shifts from persuasive communication to operational excellence and data infrastructure.
2.2 From SEO to Algorithmic Intermediation
Search Engine Optimization emerged following internet commercialization as firms developed strategies to improve visibility in search results (Enge, Spencer, Stricchiola, & Fishkin, 2015). SEO’s fundamental premise: search engines help humans discover information; firms can optimize properties to appear prominently for strategic queries. The discipline evolved from keyword-stuffing toward quality content, technical excellence, and authority building (Berman & Katona, 2013; Aswani et al., 2018).
Critical to our framework: SEO operates where humans remain ultimate decision-makers. Search engines retrieve information; humans view results, evaluate options, and decide. The algorithm’s role is limited to indexing and presenting—not deciding.
Recommendation systems research examines algorithms suggesting products based on preferences and behavior (Adomavicius & Tuzhilin, 2005; Ricci, Rokach, & Shapira, 2015). This illuminates algorithmic mediation of consumer choice, though typically in assistive rather than autonomous capacity. Recommendation systems narrow options and suggest; humans ultimately decide. In agent-mediated commerce, this assumption fails. Agents don’t recommend; they decide. This distinction is consequential.
Platform economics provides concepts for understanding markets where stakeholder groups interact through intermediating platforms (Rochet & Tirole, 2003; Eisenmann et al., 2006). Traditional platforms connect buyers and sellers. Agent-mediated commerce introduces novel configuration: (1) human consumers delegating authority, (2) autonomous agents executing on behalf of consumers, (3) firms seeking product selection, and (4) platforms providing infrastructure enabling agent transactions. This complexity creates new strategic considerations around agent trustworthiness, consumer control, preventing manipulation, and balancing stakeholder interests.
2.3 The Emergence of Generative Engine Optimization
Practitioner discourse increasingly focuses on Generative Engine Optimization (GEO)—strategies ensuring brand content appears prominently in AI-generated responses to queries (Aggarwal et al., 2024; Walker Sands, 2025; HubSpot, 2024). GEO addresses scenarios where consumers use large language models to research topics, compare options, or synthesize information. Here, the agent’s role is knowledge synthesis and recommendation; consumers retain full decision authority.
Early GEO research examined which content characteristics increase citation probability. Aggarwal et al. (2024) demonstrated that specific textual enhancements—citing authoritative sources, adding quotations, incorporating statistics, improving fluency—can boost visibility in generative responses by up to 40%. Effectiveness varies across domains: citation addition particularly valuable for factual questions, quotations for historical content, statistics for legal domains.
The GEO literature reveals consistent findings. First, traditional SEO foundations remain important; generative engines often draw from high-ranking search results (Walker Sands, 2025). Second, structured data, clear writing, and authoritative sourcing significantly improve citation likelihood (HubSpot, 2024). Third, effectiveness appears domain-dependent, requiring tailored approaches (Aggarwal et al., 2024).
While GEO has received practitioner attention, academic examination remains limited. This asymmetry reinforces our focus on transactional optimization. Our contribution addresses the more complex and commercially consequential challenge of transactional agent optimization. A brand may achieve high GEO success yet fail at AIO if operational infrastructure cannot support autonomous transactions. Conversely, excellent AIO readiness may go unexploited without GEO establishing brand awareness in agents’ knowledge bases.
We propose that informational and transactional agent optimization represent complementary but distinct disciplines. Both require shifting from human-psychology-based persuasion to algorithmic-logic-based information provision. Yet they differ fundamentally in technical requirements, strategic priorities, and outcomes. This paper focuses exclusively on AIO—transactional dimension—which has received less systematic attention despite greater commercial consequence.
2.4 Consumer Decision-Making and Synthetic Shoppers
Consumer behavior research extensively documents how humans make purchase decisions, identifying rational and psychological factors driving choice (Simon, 1955; Kahneman & Tversky, 1979). Recent scholarship examines how AI assistants influence consumer decision-making (Davenport et al., 2020; Huang & Rust, 2021), documenting a fundamental shift: as consumers delegate decision authority, the locus of influence shifts from persuading humans to ensuring algorithmic selection.
Accornero’s (forthcoming 2027) work on the “Shopper Schism” provides theoretical foundation. He argues the historical fusion of consumer (individual using product) and shopper (individual purchasing it) is undergoing permanent disaggregation. In agent-mediated commerce, the algorithm becomes the shopper—the entity that searches, evaluates, selects—while the human remains the consumer. This schism necessitates fundamental reconsideration of marketing strategy, as synthetic shopper (agent) and consumer (human) possess entirely different information-processing capabilities, decision criteria, and psychological drivers.
The shopper schism has profound implications. Traditional frameworks emphasize understanding consumer needs and preferences. These remain relevant for understanding what the human values, but become insufficient for understanding how purchase decisions are made. Marketers must develop dual understanding: the human’s underlying needs (which define the agent’s optimization function) and the agent’s decision architecture (which determines how those needs translate into selections).
2.5 Theoretical Gap and Research Objectives
This review reveals a significant gap. While we have robust frameworks for search behavior, digital marketing, recommendation systems, platform dynamics, and marketing automation individually, we lack an integrative framework for how firms should adapt when autonomous agents become the primary interface for consumer purchases. Existing theories assume either human decision-makers with algorithmic assistance or firm-controlled algorithms optimizing firm objectives. Neither adequately addresses optimization strategies for influencing autonomous agents acting on behalf of consumers.
This paper addresses this gap by developing a comprehensive framework for Agent Intent Optimization, building on search theory’s insights about information asymmetries, SEO’s practical strategies for visibility, recommendation system research on algorithmic evaluation, platform theory’s understanding of multi-sided markets, and emerging work on synthetic shoppers.
3. CONCEPTUAL FRAMEWORK: DISTINGUISHING AIO FROM SEO AND GEO
To develop rigorous understanding of Agent Intent Optimization, we must distinguish it from predecessors: Search Engine Optimization and emergent Generative Engine Optimization. While all three address discoverability and influence in digital environments, they target fundamentally different decision-making processes and require distinct capabilities. We identify five critical dimensions: (1) decision-making entity, (2) optimization target, (3) engagement timeframe, (4) value demonstration requirements, and (5) outcome orientation.
3.1 Decision-Making Entity: Human vs. Informational vs. Transactional Evaluator
SEO operates where humans remain ultimate decision-makers. Search engines retrieve information; humans view results, evaluate, and decide. The algorithm indexes and presents—it does not purchase.
GEO addresses a transitional scenario where informational agents synthesize knowledge and recommend, but humans retain final authority. When consumers ask ChatGPT “What are the best sustainable coffee brands?” the agent generates responses citing multiple options with rationale. Humans review recommendations and decide which to pursue. The agent influences consideration but doesn’t execute transactions.
AIO addresses scenarios where transactional agents make purchase decisions with limited human oversight. The agent receives high-level instructions (“buy eco-friendly detergent fitting my preferences and budget”) and independently conducts search, evaluation, selection, and purchase completion. The human may never see products considered or evaluation criteria applied. The agent’s algorithmic logic—not human psychology—determines which products are purchased.
This distinction has profound implications. In SEO, understanding human cognition, emotion, and persuasion remains essential. In GEO, establishing content authority and earning citations requires different tactics but assumes human oversight. In AIO, understanding algorithmic evaluation logic, machine-readable data structures, and operational reliability becomes paramount. Agents cannot process emotional appeals but can parse JSON-LD specifications, verify API-accessible certifications, and optimize across quantifiable variables simultaneously.
Proposition 1: As purchase decisions shift from human consumers to autonomous agents, the relative importance of emotional appeals and brand narratives will decrease while importance of structured data and attribute transparency will increase.
3.2 Optimization Target: Attention vs. Citation vs. Selection
SEO optimizes for human attention and traffic, measured by search rankings and click-throughs. Once attention is captured, firms deploy persuasive content to close sales (Berman & Katona, 2013).
GEO optimizes for citation frequency and answer prominence in AI-generated responses, measured by how often brands appear in informational agent outputs. When consumers research product categories, GEO-optimized brands earn mentions and recommendations (Aggarwal et al., 2024).
AIO optimizes for algorithmic selection and purchase completion, measured by conversion rates—whether agents actually choose to buy the product. Since agents may never direct human attention to products or explain selection rationale, generating traffic or citations becomes less relevant than ensuring products meet evaluation criteria and transaction requirements.
Proposition 2: In agent-mediated markets, firms’ marketing investments will shift from attention-capture toward structured information provision and operational excellence.
3.3 Engagement Timeframe: Moment vs. Research Phase vs. Ongoing Relationship
SEO focuses on discrete search moments. Consumers experience needs, conduct searches, evaluate results, and decide—relatively compressed timeframes. Each search event is essentially independent (Enge et al., 2015).
GEO extends this timeframe as informational agents may synthesize knowledge across interactions and retain context. If consumers ask about coffee sustainability Monday and specific brands Tuesday, agents may reference earlier discussion. However, engagement remains advisory—agents help research but don’t maintain transactional relationships.
AIO fundamentally shifts to ongoing relationship paradigms. Autonomous agents managing household purchases don’t evaluate detergent independently each time. They maintain continuous models of needs, track performance, monitor consumption, and proactively manage replenishment. If brands experience quality issues, delivery delays, or price increases, agents may switch suppliers without human intervention. The relationship is persistent and dynamic.
This creates both opportunities and challenges. Opportunities arise from building agent “loyalty” through consistent performance—reliable brands may default to repurchase without re-evaluation. Challenges emerge from agents’ perfect memory and objective tracking—single failures become permanently recorded, potentially triggering automatic switching.
Proposition 3: Agent-mediated commerce will demonstrate higher switching rates than human-mediated commerce in categories where agents can objectively evaluate performance, but lower switching rates where agents learn stable preferences and suppliers maintain consistent quality.
3.4 Value Demonstration: Narrative vs. Evidence vs. Verification
SEO-driven content relies on persuasive narratives emphasizing benefits, telling stories, evoking emotions through language and imagery. Product descriptions highlight subjective qualities (“luxurious feel,” “elegant design”) appealing to human aesthetics (Escalas, 2004).
GEO requires evidence-based content. Informational agents favor authoritative sources, empirical data, expert testimony, and verifiable claims when synthesizing responses (Aggarwal et al., 2024). Brands cannot simply claim superiority—they must provide citations, statistics, certifications agents can incorporate into responses.
AIO demands real-time verification and machine-readable specifications. Agents don’t consume narratives or static evidence; they query APIs, verify current inventory, check real-time pricing, validate certification databases, and compare structured attributes. Value must be demonstrated through: structured specifications in Schema.org or JSON-LD; API-accessible data on pricing, availability, delivery; third-party verification agents can verify independently; and performance history agents can incorporate into selection models.
The implication: in agent-mediated markets, operational excellence and data infrastructure become marketing capabilities. Supply chain managers ensuring 99.9% inventory accuracy are now part of marketing, as agents favor suppliers whose APIs reliably reflect stock. IT architects building low-latency API endpoints enable marketing success by ensuring agents can efficiently query product information.
Proposition 4: In agent-mediated markets, product success will correlate more strongly with operational metrics than with traditional marketing metrics.
3.5 Outcome Orientation: Awareness vs. Consideration vs. Transaction
SEO success is measured by awareness and consideration metrics: search rankings, organic traffic, page views, time-on-site—reflecting success capturing human attention and bringing potential customers into digital properties (Berman & Katona, 2013).
GEO success is measured by consideration-stage metrics: citation frequency, mention sentiment, answer prominence, brand authority scores—reflecting success becoming part of agents’ knowledge synthesis process (Aggarwal et al., 2024).
AIO success is measured by transaction completion: selection rates, conversion completion, repeat purchase frequency, agent share-of-wallet—whether agents actually choose to purchase and successfully complete transactions. Awareness and consideration are necessary preconditions but insufficient—success requires agents select products and transactions execute without friction.
This demands different measurement approaches. Traditional marketing tracks multi-touch attribution across customer journeys (Li & Kannan, 2014). GEO measurement focuses on brand visibility in AI responses. AIO measurement must focus on direct agent selection rates, win rates in competitive evaluations, and repeat purchase patterns—more similar to B2B sales metrics.
Proposition 5: Firms’ marketing performance measurement will shift from awareness and consideration metrics toward direct purchase conversion and agent share-of-wallet metrics as agent-mediated commerce grows.
3.6 Integrative Framework
These five dimensions collectively define Agent Intent Optimization as distinct from SEO and emergent GEO. The framework reveals AIO represents not merely evolution of SEO or extension of GEO but fundamental reconceptualization of how firms achieve visibility and influence purchase decisions. The progression reflects evolution in algorithmic capability and autonomy:
SEO: Algorithms help humans find information → Humans decide
GEO: Algorithms synthesize information and recommend → Humans decide with AI assistance
AIO: Algorithms evaluate, select, and execute → Algorithms decide on behalf of humans
This progression suggests as agent capabilities advance and consumer trust grows, the locus of marketing influence will continue shifting from persuading humans to ensuring algorithmic selection.
4. STRATEGIC IMPERATIVES FOR AGENT INTENT OPTIMIZATION
Having distinguished AIO conceptually, we now articulate core strategic imperatives firms must address to succeed in agent-mediated markets. We identify three fundamental imperatives: algorithmic visibility, algorithmic persuasion, and algorithmic relationship-building. Each addresses a distinct challenge in influencing autonomous agents’ behavior.
4.1 Algorithmic Visibility: Ensuring Agent Discoverability
The first imperative ensures agents can discover firm products during search processes. Unlike SEO visibility (placing content before human eyes) or GEO visibility (earning citations in informational responses), algorithmic visibility requires making products discoverable to agents’ programmatic search mechanisms.
Structured Data and Schema Markup
Agents rely on structured data formats to understand product information. Schema.org markup, JSON-LD, and semantic web technologies enable agents to extract product attributes without human-level natural language understanding. Firms must implement comprehensive structured data covering all relevant product attributes.
Consider two coffee brands: Brand A has visually appealing website with evocative descriptions but minimal structured data. Brand B implemented Schema.org Product markup for every SKU, including origin, roast level, certifications, flavor profiles, and specifications in machine-readable format. When agents search for “Fair Trade certified medium roast coffee from Ethiopia,” Brand B’s products are instantly identifiable through structured queries, while Brand A’s remain invisible despite potentially meeting criteria.
API Accessibility
Beyond static structured data, agents require access to dynamic information: current pricing, real-time inventory, delivery timeframes, customization options. This must be accessible via APIs agents can query efficiently. Firms face critical decisions: build proprietary APIs requiring individual integration, or adopt standardized protocols enabling broad agent access. Early adopters providing well-documented, reliable APIs gain preferential access. Firms relying on human-accessible web interfaces risk invisibility.
Proposition 6: Firms providing standardized API access to product information and transactional capabilities will achieve higher selection rates by autonomous agents, controlling for product quality.
4.2 Algorithmic Persuasion: Influencing Agent Selection Criteria
Visibility is necessary but insufficient. Products must excel on criteria agents use for evaluation. Unlike human persuasion leveraging psychology and emotion, algorithmic persuasion requires optimizing verifiable attributes and objective performance indicators.
Attribute Excellence and Certification
Agents evaluate products against specific criteria: sustainability certifications, performance specifications, safety ratings, quality indicators. Firms must identify which attributes agents prioritize in their category and optimize those verifiably. The “race to the measurable” becomes defining: attributes that can be objectively measured, certified, and verified receive disproportionate weight. A coffee brand’s “rich, complex flavor” claim is unverifiable; its “Specialty Coffee Association score of 88” is verifiable and comparable.
Third-party certifications take on enhanced importance. B Corp certification, Fair Trade labels, Energy Star ratings provide agents with verified signals reducing information asymmetry. Firms should strategically pursue certifications aligning with target consumers’ values that agents can verify programmatically.
Proposition 7: In agent-mediated markets, the value of third-party certifications and verifiable quality indicators will increase relative to traditional brand-building investments, as agents rely more heavily on objective signals.
Competitive Positioning and Differentiation
When agents evaluate options, they perform comparative analysis across multiple attributes simultaneously. Traditional positioning emphasizes owning distinct benefits in consumer minds (Ries & Trout, 1981). Algorithmic positioning requires identifying specific attribute combinations where firms possess comparative advantage. Multi-attribute utility theory provides relevant frameworks (Keeney & Raiffa, 1976). Agents evaluate products as bundles of attributes, weighting each according to consumer preferences. Products need not be optimal on every attribute—they must offer best overall combination given preference weights.
Proposition 8: Firms maintaining consistent product quality and reliable service will experience higher repeat purchase rates from autonomous agents, independent of traditional brand equity.
4.3 Algorithmic Relationship-Building: Cultivating Ongoing Agent Preference
The third imperative recognizes agent-mediated commerce involves ongoing relationships rather than discrete transactions. Agents learn from experience, update models based on performance, and develop persistent preferences.
Performance Consistency and Reliability
Agents monitor fulfillment accuracy, delivery timeliness, quality consistency, service reliability. Single negative experiences don’t necessarily trigger switching (agents may treat as outliers), but systematic underperformance or pattern breaks will. Key insight: agents maintain perfect memory and objective performance tracking. In human-mediated commerce, consumers forget, forgive, and maintain loyalty despite inconsistency. Agents don’t forget or forgive—they objectively calculate whether performance meets expectations and adjust accordingly.
This creates premium on operational excellence. Supply chain reliability, quality control, accurate inventory management, and consistent service delivery become differentiators. Firms cannot compensate for operational deficiencies through persuasive marketing.
Transaction Friction Reduction
Every transaction process obstacle creates opportunities for agents to switch suppliers. Agents favor suppliers offering: one-click API-enabled purchasing, flexible delivery with accurate timeframes, hassle-free returns with programmable APIs, subscription and auto-replenishment capabilities, and transparent pricing without hidden fees.
Proposition 9: Firms offering API-enabled transaction completion will capture higher agent share-of-wallet than firms requiring human intervention for checkout, controlling for product quality and price.
4.4 Integrative Strategic Framework
These three imperatives—algorithmic visibility, persuasion, and relationship-building—collectively define Agent Intent Optimization’s strategic domain. Success requires capabilities across all dimensions. Firms must ensure agents find products (visibility), ensure products excel on evaluation criteria (persuasion), and maintain performance encouraging repeat selection (relationship-building).
The imperatives interact synergistically. Superior performance may enhance future visibility through positive reviews and algorithmic trust signals. Strong attribute positioning may facilitate visibility by making products easier for agents to discover. Excellent transaction infrastructure may strengthen persuasion by reducing perceived purchase risk. However, weaknesses compound: invisible products gain no benefit from excellent attributes; highly visible products with subpar attributes lose in evaluation; well-positioned products failing to deliver reliably lose future opportunities.
5. THE AGENT DECISION PREFERENCE STACK: AN INTEGRATIVE FRAMEWORK
Having established AIO’s three strategic imperatives and distinguished AIO from SEO and GEO, we address a broader theoretical question: How can we develop unified understanding of agent decision-making encompassing both informational and transactional optimization within a single framework?
Empirical evidence emerging from early agent behavior studies reveals an apparent puzzle: certain factors—structured data, API accessibility, real-time inventory accuracy—exert strong influence on agent selection, while factors central to traditional marketing theory—brand equity, emotional appeals, creative messaging—show substantially weaker effects (Aggarwal et al., 2024). This pattern holds across both informational agents (GEO context) and transactional agents (AIO context), suggesting systematic shift in how decisions are made when algorithmic evaluation replaces human judgment.
We propose the Agent Decision Preference Stack as integrative framework explaining this shift and positioning both informational and transactional optimization within unified theoretical structure.
5.1 The Three-Layer Architecture
The Preference Stack posits that autonomous agents organize decision-making across three hierarchical layers, each operating on different information types and exerting distinct influence on outcomes. This layered architecture reflects established principles from constraint satisfaction theory (Tsang, 1993), multi-attribute utility theory (Keeney & Raiffa, 1976), and hierarchical decision modeling (Simon, 1962), adapted to autonomous agent contexts.
Layer 1: Foundational Directives (Hard Constraints)
Non-negotiable requirements explicitly defined by users or system-level rules. They function as Boolean gates—options either satisfy all constraints or are immediately eliminated. Layer 1 includes: budget constraints (“Don’t exceed $50”), safety requirements (“Must be peanut-free”), compatibility needs (“Must work with iOS 17”), regulatory compliance (“Must meet GDPR requirements”), delivery requirements (“Must arrive by Friday”), and ethical constraints (“No products tested on animals”).
Layer 1 operates on binary logic. A product costing $51 when budget limit is $50 is eliminated regardless of quality advantages. Agents cannot make tradeoffs at this layer—constraints are inviolable until explicitly modified by human principals.
Layer 2: Inferred Knowledge (Learned Heuristics)
Once options satisfy Layer 1 constraints, agents apply learned heuristics to evaluate and rank options. Layer 2 incorporates: brand reputation (aggregated quality signals and trust indicators), expert recommendations (citations and endorsements from authoritative sources), review patterns (statistical analysis of user feedback), category knowledge (learned associations between attributes and quality), historical performance (patterns from previous purchases), and domain expertise (industry-specific evaluation criteria).
Layer 2 is where GEO primarily operates. Content establishing brand authority, earning expert citations, and appearing in informational agent responses influences heuristics agents apply during evaluation. Brands frequently mentioned in sustainability discussions receive positive weight in Layer 2 evaluation when agents process requests emphasizing environmental criteria.
Layer 2 doesn’t eliminate options but creates preference orderings. Lesser-known brands can still compete if they excel at Layer 3, but start from disadvantaged positions if Layer 2 heuristics favor established competitors.
Layer 3: Real-Time Optimization (Final Calculation)
The final layer performs real-time optimization across remaining options using current-state information: current pricing (including temporary promotions and dynamic discounts), actual availability (real-time inventory status), delivery timeframes (calculated from current location and logistics), transaction ease (API accessibility and checkout friction), service reliability (recent performance metrics), and fulfillment track record (actual on-time delivery rates).
Layer 3 is where AIO dominates. Operational excellence—accurate inventory systems, competitive pricing, fast delivery, reliable APIs—determines final selection. Products with slight Layer 2 disadvantages (less brand recognition) can win at Layer 3 through superior operational performance.
5.2 Framework Application: A Detailed Example
Consider an autonomous agent tasked with purchasing coffee: “Buy me high-quality Fair Trade coffee, delivered by Friday, budget $25.”
Layer 1 Processing: The agent immediately filters coffee products satisfying hard constraints: must have verified Fair Trade certification (Boolean: yes/no), must cost ≤ $25 including delivery (Boolean: yes/no), must deliver by Friday (Boolean: yes/no). All non-compliant options are eliminated instantly.
Layer 2 Processing: Among constraint-satisfying options, the agent applies learned heuristics: brands with higher aggregate ratings receive preference, Fair Trade certification authenticity is weighted, brands frequently cited in coffee expert content (GEO influence) receive authority boost, recent negative reviews trigger caution, certain origin regions may be associated with quality in learned preferences. This creates preference ordering.
Layer 3 Processing: The agent queries current state: which options are currently in stock? What are exact current prices (including real-time promotions)? What are actual delivery timeframes from current location? Which sellers have APIs enabling instant purchase vs. requiring manual checkout? Which have had recent successful transactions?
The agent executes final optimization: perhaps Brand A has slightly better ratings (Layer 2 advantage) but Brand B is $4 cheaper, has guaranteed Friday delivery, and offers one-click API purchase (Layer 3 advantages). Brand B wins the transaction.
5.3 Implications for GEO and AIO Strategy
The Preference Stack provides strategic clarity about where different optimization efforts create value:
GEO Operates Primarily at Layer 2
Generative Engine Optimization influences what agents “know” about quality, best practices, and trusted sources. When agents synthesize knowledge—“What are the best coffee brands?”—they draw on Layer 2: learned heuristics from expert content, review aggregation, authoritative sources.
Strategic Implication: GEO success requires establishing content authority, earning citations from agents’ training data or real-time knowledge sources, and creating structured, evidence-based content shaping agent heuristics.
Limitation: Layer 2 influence cannot overcome Layer 1 constraint violations, and may be overridden by Layer 3 operational factors. Brands may be “known” as high-quality yet lose transactions to operationally superior competitors.
AIO Operates Primarily at Layer 3
Agent Intent Optimization influences moment-of-transaction selection through operational excellence. When agents execute purchases, Layer 3 factors—current price, availability, delivery capability, transaction ease—often prove decisive.
Strategic Implication: AIO success requires API accessibility, real-time data accuracy, competitive operational performance, and transaction infrastructure enabling frictionless completion.
Limitation: Layer 3 excellence without Layer 2 credibility may result in invisibility—if heuristics eliminate brands from consideration, operational capabilities never get evaluated. However, Layer 3 performance feeds back to Layer 2 over time.
Both GEO and AIO Must Address Layer 1
Neither can overcome failure to satisfy hard constraints. Products violating budget limits, safety requirements, or compatibility needs are eliminated before any optimization strategy takes effect. Both informational and transactional optimization must begin with ensuring constraint compliance.
5.4 Testable Propositions from the Preference Stack
The framework generates empirically testable propositions:
Proposition 10 (Layer Dominance): In agent-mediated transactions, Layer 3 factors will exert stronger influence on final selection than Layer 2 factors, controlling for Layer 1 constraint satisfaction.
Proposition 11 (Category Moderation): Product category will moderate layer importance. Credence goods and complex products will show stronger Layer 2 effects. Search goods and commodified products will show stronger Layer 3 effects.
Proposition 12 (Dynamic Learning): Over time, Layer 3 performance will influence Layer 2 status. Agents will update heuristics based on transaction outcomes, creating feedback loops where operational excellence builds reputational benefits.
Proposition 13 (Constraint Primacy): Layer 1 violations will result in immediate elimination regardless of Layer 2 or Layer 3 advantages.
Proposition 14 (Optimization Efficiency): Firms optimizing at layers where they have competitive disadvantage will achieve higher ROI than firms optimizing at layers where they already possess advantage.
5.5 Reconciling Counterintuitive Findings
The Preference Stack explains patterns appearing counterintuitive from traditional marketing perspectives:
Why Brand Equity Shows Weaker Effects
Traditional theory emphasizes brand equity as key purchase driver (Keller, 1993; Aaker, 1991). Yet early evidence suggests brand equity exerts less influence in agent-mediated than human-mediated purchases.
Preference Stack Explanation: Brand equity operates primarily at Layer 2 (learned quality heuristics). While this influences consideration set formation, Layer 3 factors exert stronger influence on final selection. Agents weight current-state information more heavily than historical reputation when both are available.
Why Structured Data Matters More Than Creative Content
Traditional digital marketing emphasizes engaging content, compelling copywriting, emotional appeals. Yet agent optimization requires structured data and specifications.
Preference Stack Explanation: Creative content influences human psychology but doesn’t map to any Preference Stack layer. Structured data enables Layer 1 constraint verification, Layer 2 attribute comparison, and Layer 3 real-time optimization. Agents cannot process emotional appeals but can parse JSON-LD specifications.
Why Operational Excellence Increasingly Determines Outcomes
Supply chain capabilities, inventory management, fulfillment speed have traditionally been operational concerns separate from marketing. Yet these factors strongly predict agent selection.
Preference Stack Explanation: These operational factors operate at Layer 3, which executes final decision. As agents handle more transactions, Layer 3’s decisive role becomes more apparent. Marketing strategy must now encompass operational excellence—significant departure from traditional domain boundaries.
5.6 Theoretical Contributions of the Preference Stack
The framework makes several contributions to marketing theory:
1. Unifies Informational and Transactional Optimization
Rather than treating GEO and AIO as separate phenomena, the Preference Stack positions both within unified decision architecture, explaining where each creates value and how they interact.
2. Predicts the Operational Turn in Marketing
The framework explains why marketing success in agent-mediated commerce increasingly depends on capabilities (API development, supply chain excellence, real-time data systems) traditionally outside marketing’s domain. As Layer 3 becomes decisive, marketing must expand its scope.
3. Explains Dynamic Feedback Between Layers
The framework accounts for how performance at one layer influences status at other layers over time. Operational excellence (Layer 3) builds reputation (Layer 2), which may eventually influence whether consumers set constraining preferences (Layer 1) favoring the brand.
4. Provides Falsifiable Predictions
Unlike purely descriptive frameworks, the Preference Stack generates specific, testable predictions about relative effect sizes, category moderators, and temporal dynamics that can be empirically validated.
6. PROOF-OF-CONCEPT VALIDATION
This section provides illustrative evidence that the three-layer Preference Stack framework is both computationally implementable and descriptively accurate. We present abbreviated findings from two complementary approaches; comprehensive empirical validation is reserved for future work.
6.1 Computational Feasibility
We constructed a simplified agent decision model implementing the three-layer architecture (200 trials, 15 coffee products, 600 decisions). The simulation focused on coffee purchases to enable direct comparison with the theoretical example presented in Section 5.2.
Key Findings:
• Layer 3 Dominance: Layer 3 (operational factors) exerted 4.7× stronger influence than Layer 2 (reputation) on final selection (p < .001). Selected products showed Layer 3 scores averaging 0.970 (normalized 0-1 scale) compared to Layer 2 scores of 0.206.
• Operational Excellence Overcomes Reputation Deficits: Products with maximum Layer 3 scores (operational excellence = 1.0) won selection regardless of Layer 2 status (brand reputation ranging from 0.0 to 1.0), validating that operational superiority can overcome low brand recognition.
• Compensatory Decision-Making: While Layer 3 dominated, exceptional Layer 2 performance (premium brands with reputation ≈ 1.0) could win despite moderate Layer 3 scores (0.70-0.85), demonstrating nuanced tradeoff evaluation rather than mechanical optimization.
• Architecture Feasibility: The three-layer model produced theoretically predicted decision patterns, demonstrating computational feasibility of the framework.
These results provide proof-of-concept that the framework generates testable, quantifiable predictions about agent behavior and that a three-layer architecture successfully models agent decision processes.
6.2 Descriptive Accuracy
Analysis of three AI shopping assistants (ChatGPT, Perplexity, Claude) across 30 scenarios revealed systematic three-layer processing:
• Layer 1 (Constraint Checking): 63.3% of scenarios • Layer 2 (Reputation Assessment): 80.0% of scenarios
• Layer 3 (Operational Verification): 66.7% of scenarios
Current informational agents show balanced layer processing, consistent with their advisory role. The high Layer 2 prevalence (80.0%) indicates these agents heavily leverage brand names, review scores, and quality signals when formulating recommendations—consistent with their current role as informational advisors helping humans evaluate options.
Agent-specific patterns revealed: ChatGPT demonstrated uniform attention across all three layers (70% each), Perplexity showed operational emphasis (70% Layer 2 and Layer 3), while Claude exhibited strong reputation focus (100% Layer 2 mentions).
The framework predicts Layer 3 will dominate as agents gain transactional autonomy—a testable proposition for future empirical work. The case study demonstrates that observable AI shopping assistants exhibit the three-layer processing patterns predicted by the framework, providing descriptive validation.
6.3 Limitations and Future Research Agenda
This proof-of-concept validation demonstrates feasibility but requires substantial extension:
Essential Future Work: - Large-scale empirical validation across diverse product categories - Longitudinal observation of actual agent-mediated transactions - Experimental manipulation of layer factors for causal identification - Cross-platform comparison as agent architectures evolve - Examination of category moderators and boundary conditions - Investigation of consumer welfare implications - Organizational restructuring requirements and capabilities
We position this framework as theoretical foundation requiring comprehensive empirical program. The SSRN prepublication establishes conceptual priority while inviting scholarly collaboration on validation efforts.
7. DISCUSSION AND IMPLICATIONS
This conceptual framework makes several theoretical contributions to marketing scholarship. While comprehensive empirical validation remains essential, the illustrative findings provide initial support for the framework’s core predictions and demonstrate its utility for organizing future research.
7.1 Theoretical Implications
Reconceptualizing the Marketing Domain
The Agent Decision Preference Stack challenges traditional boundaries of marketing competency. Marketing has historically focused on communication, persuasion, and brand building—capabilities centered on influencing human psychology. When agents mediate purchases, operational excellence at Layer 3 becomes a marketing capability. API development, supply chain reliability, inventory accuracy, and transaction infrastructure transition from “back-office” operations to revenue-driving marketing assets.
This reconceptualization aligns with Day’s (2011) call for marketing to develop new capabilities in response to market transformation, but extends beyond his focus on customer insight and relationships to encompass technical and operational domains. Marketing organizations must integrate expertise in data engineering, systems architecture, and operations management—significant organizational redesign challenge.
Extending Search Theory to Autonomous Agents
Stigler’s (1961) search theory assumes human searchers balance search costs against expected benefits. Autonomous agents fundamentally alter this calculus. Agents face near-zero marginal search costs (can query thousands of options instantly), perfect memory (no cognitive limitations), and computational optimization capabilities exceeding human capacity. This transformation suggests search costs no longer limit market efficiency in agent-mediated commerce.
However, new frictions emerge. Agents face information accessibility constraints—products without structured data or API interfaces remain effectively invisible despite potentially meeting consumer needs. The “cost” of search shifts from time and effort to data interoperability and platform integration. Future search theory must account for these new friction sources.
Information Economics in Agent-Mediated Markets
Akerlof’s (1970) analysis of information asymmetry assumes human buyers cannot assess quality before purchase. Agents potentially reduce these asymmetries by aggregating post-purchase reviews, verifying certifications, and accessing third-party test data. However, new asymmetries emerge between firms providing machine-readable transparency and those obscuring information in human-accessible but agent-inaccessible formats.
Spence’s (1973) signaling theory requires reconsideration. Traditional quality signals (advertising expenditure, premium pricing, celebrity endorsements) do not translate to agent evaluation. New signals emerge: API availability signals technical sophistication, real-time data accuracy signals operational competence, third-party certifications signal verified quality. The cost of signaling shifts from persuasive communication to operational and technical investments.
Platform Theory and Multi-Stakeholder Dynamics
Agent-mediated markets represent novel platform configuration with four stakeholder groups: consumers who delegate authority, agents who execute decisions, firms seeking selection, and platforms providing infrastructure. Traditional two-sided or three-sided market models prove insufficient.
This complexity introduces new strategic considerations. Platform governance becomes critical—how do platforms ensure agents faithfully serve consumer interests rather than platform interests? What disclosure requirements apply when agents receive commissions for product recommendations? How do regulatory frameworks address agent conflicts of interest?
7.2 Practical Implications for Marketing Managers
Redefining Marketing Metrics and Measurement
Traditional marketing metrics (awareness, consideration, preference) become insufficient when agents mediate transactions. Marketing measurement must evolve to track: algorithmic visibility metrics (structured data completeness, API uptime, platform integration coverage), agent selection rates (percentage of times brand is selected when agent evaluates category), agent share-of-wallet (revenue from agent-mediated transactions relative to total agent-mediated category spending), transaction completion rates (percentage of agent-initiated purchases successfully completing), and repeat agent selection (frequency of automatic repurchase without re-evaluation).
Organizational Restructuring for Agent Readiness
Successfully competing in agent-mediated markets requires organizational changes: Marketing-IT Integration (marketing teams need embedded data engineers and technical architects), Marketing-Operations Partnership (supply chain, fulfillment, and customer service become marketing functions when operational excellence drives agent selection), New Roles (“Agent Optimization Manager” positions emerge requiring hybrid marketing-technical expertise), and Cross-Functional Alignment (product development, IT, operations, and marketing must coordinate around agent requirements rather than operating in silos).
Investment Prioritization
The Preference Stack framework provides prioritization tool for marketing investments. For brands with strong Layer 2 positioning (high reputation, strong GEO performance): prioritize Layer 3 investments in API development, operational excellence, and transaction infrastructure. For brands with weak Layer 2 positioning: pursue dual strategy—Layer 2 investments in thought leadership and GEO while simultaneously building Layer 3 capabilities. For brands with Layer 1 constraint challenges: address constraint compliance first through product development or strategic positioning.
7.3 Public Policy and Regulatory Implications
The rise of autonomous shopping agents raises significant policy questions around consumer protection, competition policy, and data privacy. Current consumer protection frameworks assume humans make purchase decisions with full information. When agents mediate, concerns arise around transparency requirements for evaluation criteria, conflicts of interest disclosure, error liability allocation, and consumer opt-out rights.
Agent-mediated commerce may concentrate market power in agent platform providers, raising antitrust concerns around platform dominance, self-preferencing prohibitions, exclusive arrangement scrutiny, and incumbent data advantages. Autonomous agents require extensive consumer data to function effectively, raising questions about data collection boundaries, cross-context tracking regulation, and secondary use restrictions.
7.4 Societal and Ethical Implications
Beyond immediate business and policy concerns, agent-mediated commerce raises deeper questions. Does delegating purchase decisions to algorithms enhance consumer welfare (by saving time and optimizing choices) or diminish human agency (by removing active decision-making from daily life)? Will agent-mediated commerce reduce product variety by favoring measurable attributes over experiential qualities? Does algorithmic selection advantage established brands with strong data infrastructure over innovative newcomers? Do firms lacking technical capabilities face systematic disadvantage, creating barriers for small businesses, artisans, and producers in developing economies? How do we ensure agent-mediated commerce doesn’t exacerbate sustainability challenges when agents optimize for consumer preferences while potentially underweighting externalities?
8. CONCLUSION
The rise of autonomous AI agents as “synthetic shoppers”—consumer shopping intermediaries—represents fundamental transformation in how markets function. When agents conduct product search, evaluation, and purchase on behalf of human principals, the strategic calculus for firms changes dramatically. Marketing, which has historically focused on influencing human cognition and emotion, must now address the challenge of influencing algorithmic evaluation processes.
This paper has introduced Agent Intent Optimization (AIO) as a novel strategic marketing discipline addressing this transformation. Through conceptual analysis grounded in search theory, information economics, platform strategy, and digital marketing scholarship, we have distinguished AIO from traditional Search Engine Optimization and emergent Generative Engine Optimization across five fundamental dimensions: decision-making entity, optimization target, engagement timeframe, value demonstration requirements, and outcome orientation.
We have articulated three core strategic imperatives for AIO success: algorithmic visibility (ensuring agents can discover products), algorithmic persuasion (influencing agent evaluation criteria), and algorithmic relationship-building (cultivating ongoing agent preference). These imperatives collectively define the strategic domain firms must master to succeed in agent-mediated markets, supported by fourteen testable propositions for empirical investigation.
Our principal theoretical contribution is the Agent Decision Preference Stack—an integrative framework positioning both informational and transactional optimization within unified theory of how autonomous agents organize decision-making across three layers: foundational directives (hard constraints), inferred knowledge (learned heuristics), and real-time optimization (final calculation). This framework explains why operational excellence increasingly trumps traditional brand equity in agent-mediated purchases, a counterintuitive finding from conventional marketing perspectives that nonetheless aligns with how algorithmic systems actually function.
For practitioners, the framework offers structured approach to assessing agent-readiness across all three layers of the Preference Stack, while clarifying that GEO and AIO represent complementary rather than competing strategies. A firm may excel at GEO (establishing authority in agent knowledge bases) yet fail at AIO if operational infrastructure cannot support autonomous transactions. Conversely, excellent AIO readiness may go unexploited without GEO efforts establishing baseline credibility and consideration set inclusion.
Illustrative empirical validation through computational simulation and case study analysis provides converging evidence for the framework’s validity. The simulation demonstrated that agents implementing the three-layer architecture prioritize operational excellence (Layer 3) over brand reputation (Layer 2) by a factor of 4.7× when making autonomous purchase decisions. The case study revealed that current AI shopping assistants (ChatGPT, Perplexity, Claude) systematically demonstrate three-layer processing patterns. The high Layer 2 prevalence in current informational agents, contrasted with Layer 3 dominance in the transactional simulation, illuminates the framework’s central insight: as agents transition from advisory to autonomous roles, operational excellence will increasingly trump traditional brand equity in determining market success.
Several limitations constrain our work’s scope. As conceptual framework with illustrative validation, comprehensive empirical testing remains essential. The three-layer Preference Stack simplifies what may be more complex agent decision architectures. Agent capabilities continue evolving rapidly, potentially requiring framework adaptation. Product categories likely moderate the relative importance of different layers and strategies. Our framework assumes substantial agent autonomy; when humans remain actively involved, dynamics may differ.
The implications extend beyond marketing strategy to organizational design, public policy, and societal welfare. Marketing organizations must integrate technical, operational, and analytical capabilities previously outside their scope. Policymakers must develop frameworks addressing consumer protection, competition, and privacy in agent-mediated markets. Society must grapple with philosophical questions about consumer autonomy, market diversity, and digital inclusion as algorithms increasingly mediate economic decisions.
Despite these challenges, one conclusion is clear: the transition to agent-mediated commerce is not speculative but inevitable. The convenience, efficiency, and optimization that autonomous agents provide represent value proposition too compelling for consumers to resist. The window for strategic action is now. Leaders who grasp this transformation and develop capabilities across the three layers of the Preference Stack will not merely survive the disruption—they will define the future of commerce.
As Accornero (forthcoming 2027) argues in his examination of the Shopper Schism, the permanent disaggregation of consumer and shopper forces firms to elevate themselves to domains machines cannot touch: creativity, strategy, and authentic value creation. The algorithms will handle the optimization. It is up to marketing leaders to ensure there is something genuinely worthy of being chosen.
AUTHOR’S NOTE
This working paper presents a conceptual framework for Agent Intent Optimization with illustrative proof-of-concept validation. The framework generates fourteen testable propositions for empirical investigation.
The author welcomes: - Feedback on the theoretical framework - Collaboration opportunities for empirical validation - Industry partnerships for access to agent transaction data - Comments on propositions and research design
Comprehensive empirical validation is in progress. Updated versions will be posted to SSRN as the research program advances.
The author confirms sole intellectual ownership of the concepts, frameworks, and all substantive arguments developed herein. AI was used as an assistive tool in research and drafting certain portions of the manuscript, but all original theoretical contributions are those of the author, who retains full responsibility for content and conclusions. Disclosure is provided in line with current academic and publishing ethics guidelines. The author is currently expanding these theoretical frameworks in a forthcoming book on algorithmic commerce under contract with St. Martin's Press, expected 2027.
ACKNOWLEDGMENTS
The author thanks colleagues and reviewers who provided feedback on earlier drafts. All errors remain the author’s responsibility.
DATA AVAILABILITY
Upon request, the author will provide: - Simulation code (Python) with documentation - Case study protocols and coding scheme - Supplementary materials
Future empirical work will include full data and replication materials per open science standards.
REFERENCES
Aaker, D. A. (1991). Managing Brand Equity: Capitalizing on the Value of a Brand Name. New York: The Free Press.
Accornero, P. F. (forthcoming 2027). The Algorithmic Shopper: Marketing and Selling in the Age of AI Gatekeepers. New York: St. Martin’s Press (Macmillan).
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024). GEO: Generative Engine Optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5-16). Barcelona, Spain: ACM.
Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics, 84(3), 488-500.
Aswani, R., Kar, A. K., Ilavarasan, P. V., & Dwivedi, Y. K. (2018). Search engine marketing is not all gold: Insights from Twitter and SEOClerks. International Journal of Information Management, 38(1), 107-116.
Berman, R., & Katona, Z. (2013). The role of search engine optimization in search marketing. Marketing Science, 32(4), 644-651.
Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24-42.
Day, G. S. (2011). Closing the marketing capabilities gap. Journal of Marketing, 75(4), 183-195.
Eisenmann, T., Parker, G., & Van Alstyne, M. (2006). Strategies for two-sided markets. Harvard Business Review, 84(10), 92-101.
Enge, E., Spencer, S., Stricchiola, J., & Fishkin, R. (2015). The Art of SEO (3rd ed.). Sebastopol, CA: O’Reilly Media.
Escalas, J. E. (2004). Narrative processing: Building consumer connections to brands. Journal of Consumer Psychology, 14(1-2), 168-180.
Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30-50.
HubSpot. (2024). Generative engine optimization: What we know so far. HubSpot Marketing Blog. Retrieved from https://blog.hubspot.com/marketing/generative-engine-optimization
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Keeney, R. L., & Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Trade-Offs. New York: Wiley.
Keller, K. L. (1993). Conceptualizing, measuring, and managing customer-based brand equity. Journal of Marketing, 57(1), 1-22.
Lamberg, J. A., & Tikkanen, H. (2006). Changing sources of competitive advantage: Cognition and path dependence in the Finnish retail industry 1945-1995. Industrial and Corporate Change, 15(5), 811-846.
Li, H., & Kannan, P. K. (2014). Attributing conversions in a multichannel online marketing environment: An empirical model and a field experiment. Journal of Marketing Research, 51(1), 40-56.
Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311-329.
Oestreicher-Singer, G., & Sundararajan, A. (2012). The visible hand? Demand effects of recommendation networks in electronic markets. Management Science, 58(11), 1963-1981.
Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender systems: Introduction and challenges. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (2nd ed., pp. 1-34). Boston: Springer.
Ries, A., & Trout, J. (1981). Positioning: The Battle for Your Mind. New York: McGraw-Hill.
Ringberg, T., Reihlen, M., & Rydén, P. (2019). The technology-mindset interactions: Leading to incremental, radical or revolutionary innovations. Industrial Marketing Management, 79, 102-113.
Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the European Economic Association, 1(4), 990-1029.
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99-118.
Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467-482.
Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355-374.
Spence, M. (2002). Signaling in retrospect and the informational structure of markets. American Economic Review, 92(3), 434-459.
Stigler, G. J. (1961). The economics of information. Journal of Political Economy, 69(3), 213-225.
Tsang, E. (1993). Foundations of Constraint Satisfaction. London: Academic Press.
Walker Sands. (2025). Generative engine optimization (GEO): What to know in 2025. Walker Sands Communications. Retrieved from https://www.walkersands.com/about/blog/generative-engine-optimization-geo-what-to-know-in-2025/
Word Count: ~8,500 words
END OF WORKING PAPER
