This blog is a monster 25 pages. Hence the executive summary

Transformation seldom starts at the core of an existing market or business, but rather starts in edge use cases of unmet needs and price performance (ie, Innovators Dilemma). Edge UCs may be the biggest economic opportunity for Agentic, organizing hyperlocal, DTC and the food chain to create a new kind of market. If these edge UCs can be addressed in the US, it also greatly expands the global market opportunity as small regional needs resemble the edge.

The consumer benefit of this approach is an awareness of the very different “purpose” of agentic platforms vs Amazon or Walmart.  Imagine identifying which businesses and service providers could address your needs locally. 

Executive Summary

1. Strategic Focus on Edge Markets: The primary economic opportunity for Agentic Commerce lies in “edge” use cases (Direct-to-Consumer, hyperlocal, custom goods, circular economy) rather than direct competition with retail giants. These peripheral markets have significant unmet needs and fewer incumbent barriers.

2. Innovator’s Dilemma Application: This approach aligns with the Innovator’s Dilemma, where transformative technologies often incubate in niche areas before disrupting core markets.

3. Core Economic Activity is Market Creation: In these initial stages, the fundamental role of agentic AI is to enable new types of transactions and organize currently inefficient or fragmented markets, effectively creating new economic ecosystems.

4. Key US Edge Segments Identified:

• • Direct-to-Consumer (DTC): Agentic AI can lower operational barriers for brands, especially SMBs.
  • Hyperlocal (Food, Services, Local Inventory): AI can organize fragmented supply, optimize logistics, and reduce waste.
  • Custom-Made & Curated Goods: AI can manage complex design, sourcing, and production for bespoke items.
  • Circular Economy (Used/Loanable Goods): AI can reduce friction in listing, discovery, and transactions for pre-owned items.

5. Critical Challenges for Platform Operators:

• • Capturing and Structuring Supply: Aggregating diverse product/service information from numerous SMBs and enabling real-time inventory visibility.
  • Orchestrating End-to-End Customer Experience: Managing the entire journey from discovery to fulfillment and post-purchase support, ensuring quality and reliability in decentralized networks.
  • Defining Market Dynamics: Establishing mechanisms for pricing (beyond fixed lists, including auctions/RFPs), risk allocation, and building trust comparable to established retailers.

6. Greater Need, Greater Opportunity: Unstructured edge markets have a more profound need for AI-driven orchestration. The significant effort required to build these markets creates substantial and defensible value for successful platform operators.

7. Global Blueprint Potential: Success in organizing US hyperlocal markets can provide a model for expansion into emerging economies (e.g., Africa, LATAM), which often feature similar fragmentation and mobile-first environments.

8. Conclusion: Agentic commerce’s path to scale involves solving complex problems in underserved markets. The “hard work” of market creation in these areas will lead to significant, defensible economic opportunities and a foundation for global impact.

Why Not Core Retail?

Clayton Christensen’s seminal work, “The Innovator’s Dilemma,” provides a compelling framework for understanding existing businesses optimize for serving their existing customer base. They often overlook or dismiss disruptive technologies in niche markets or cater to overlooked customer segments. Competitors attack from below here, addressing unmet needs where current solutions are inadequate, overly complex, or prohibitively expensive. 

The “unmet need” in these peripheral markets frequently extends beyond the mere provision of a product; it often includes discovery, service, and trust within fragmented and inefficient markets. For instance, the challenge in hyperlocal supply chains is not necessarily a lack of local produce, but the absence of an efficient system to connect producers with consumers at scale (ie farmers’ markets don’t scale). Agentic AI’s capability to orchestrate is not just task execution but comprehensive market orchestration. 

The “price performance” advantage offered by agentic AI in these scenarios may initially develop not as better price, but rather in lower overall transaction costs (such as search, negotiation, and coordination efforts) across markets with little structure (ie complex, customized, or difficult-to-source goods and services). This price-performance dimension can also be realized through hyper-personalization at scale, a superior value proposition that incumbent players cannot easily replicate. This form of “performance” is redefined as “fit-for-purpose,” where agentic AI excels.

Incumbent Resistance and Competency. You Can’t Beat Amazon in Product Search!

New technological innovations are frequently first adopted by established market players to bolster their competitive positions within existing market frameworks. Consequently, Perplexity or other agentic aggregators aggregating demand will  encounter substantial resistance. Current stakeholders, including retailers and distributors, actively work to protect their established market shares and customer relationships. As I outlined previously, CMOs today express apprehension regarding the emergence of “another Google”. 

By initiating operations in edge markets agentic commerce platforms can minimize direct confrontation with entrenched players. This strategy allows the new platform to cultivate its own network effects and refine its value proposition before potentially challenging more fortified market positions. 

Story - Matching supply and demand is only part of the problem, the next is pricing and risk.  Back in 2001 B2B exchanges were the fad, COVISINT automotive exchange was our largest at Oracle. The first auction that COVISINT held was for tires. Michelin won the RFP but they were furious. “We have worked with Ford for 80 yrs, built our plant right next to their factory. I just can’t believe this is how they want to partner with us”. Michelin sent their first spec tires to Ford and emblazoned on the tire was the brand “TIRES” with no sign mention of Michellin. Ford immediately called up to complain. Michelin said that the RFP did not require a brand and these tires were within the specs of the RFP. Ford replied “we can’t put these on a vehicle, consumers would never but it”. Michelin responded, we will be glad to send our branded tires over at our normal cost. 

This story illustrates how deeply ingrained relationships, brand value, and expectations shape incumbent behavior and their perception of value.  We all know Fortune 500 firms suffer from “organizational inertia,” and fined it difficult to pivot when new technologies emerge that threaten their core business models. Edge markets, by their nature, possess fewer entrenched systems and practices. In these peripheral markets, agentic platforms can strategically reframe their role. Instead of being perceived as disintermediators of strong, existing players, they can position themselves as enablers for underserved small and medium-sized businesses (SMBs) and individual producers. 

This strategy should fosters collaboration rather than immediate conflict. For a local artisan, a custom furniture maker, or a hyperlocal farmer struggling with market access and visibility, an agentic platform can offer an invaluable route to market that is currently unavailable. The platform thus becomes an ally, empowering these smaller entities to reach new customers or compete more effectively, rather than posing a direct threat to an established intermediary. This dynamic fundamentally alters the challenge of network building from one of confrontation to one of mutual benefit.

The “Everything Else” Economy. 

IMHO the initial opportunity for agentic commerce lies in the vast, often unorganized, “everything else” economy. These are markets characterized by fragmentation, specificity, and unmet needs that fall outside the purview of mass-market retail. AI-driven agents are uniquely positioned to bring structure, efficiency, and new value to these segments.

1. Direct-to-Consumer (DTC)

Today, where do you search for hyperlocal or DTC?

The Direct-to-Consumer (DTC) model represents one of the most rapidly expanding segments in the retail landscape, with brands connecting with their end customers. This “milk man” approach has found significant traction in e-commerce (ex Warby Parker and Dollar Shave Club), and holds potential in hyperlocal contexts, including fresh produce, bakery items, dairy products, and the inventory of local stores. Projections indicate DTC e-commerce sales in the US were expected to reach $161.22 billion by 2024, underscoring a persistent shift towards direct selling. Further growth is anticipated, with social commerce, a key enabler for DTC, projected to generate over $100 billion in revenue in 2025, a 22% increase from 2024.

Agentic commerce can provide structure for  DTC by automating and optimizing numerous the DTC journey. This includes highly personalized product discovery and demand generation (distinct from traditional advertising), streamlined order management, and responsive customer service. Such capabilities can significantly lower the barrier to entry for brands to adopt and scale DTC more effectively. Today the challenge often extends beyond merely selling directly; it encompasses managing the entire customer lifecycle and the operational complexities that retailers traditionally handle. Agentic AI can function as an “outsourced operational backbone,” assisting with tasks like autonomous product catalog management, where agents monitor supplier feeds and sales data to keep offerings fresh, and delivering hyper-personalized shopping experiences. 

This support can empower a new wave of “micro-DTC” brands, particularly for niche and artisanal producers who currently lack the scale, technical expertise, or operational capacity to manage direct selling operations effectively. By radically simplifying the creation and management of these operations, agentic platforms can unlock a vast, currently inaccessible supply from the long tail of producers.

2. Hyperlocal Markets (Food, Services, Local Inventory)

Hyperlocal supply chains, particularly for perishable goods such as locally sourced grains, fresh produce, and meats, are frequently characterized by disorganization and inefficiency. Traditional models like farmers’ markets, while valuable for community engagement, lack the scalability and structured to meet broader demand. The global online food delivery market, a significant component of hyperlocal commerce, was projected to see revenues grow from $156.75 billion in 2024 to $173.57 billion in 2025.10 The US food subscription market alone was valued at $150.3 billion in 2024, with a forecasted compound annual growth rate (CAGR) of 9.6% from 2025 to 2032.1 The broader gig economy, which encompasses a wide array of local services, was valued globally at $455 billion in 2025, with the US market segment estimated at $556.7 billion in 2024 and projected to grow to $646.77 billion in 2025, exhibiting a CAGR of 16.18%. These figures underscore the opportunity.

A platform capable of aggregating local supply or real-time inventory could focus on enabling consumers to buy locally and orchestrate both the purchase process and the logistics of local delivery.  This unmet need extends beyond tangible goods to encompass local services from home maintenance to personal wellness. The hyperlocal market opportunity lies in significantly reducing spoilage and waste, particularly for perishable items, and increasing asset utilization for small service providers. This is achieved through demand forecasting, efficient inventory management, and optimized logistical coordination, all powered by AI. For small local businesses, unsold inventory or unbooked service appointments represent direct losses. AI’s capacity to aggregate demand, predict consumer needs, and streamline fulfillment directly addresses these inefficiencies, converting potential waste into revenue.

A hyperlocal agentic platform could catalyze the formation of new “micro-economies.” or at least Micro Markets. By lowering the barriers to entry for supplying goods or services at a local level. This impact transcends mere efficiency gains for existing businesses; it fosters an expansion of the participatory base in local commerce, potentially enhancing local employment and the global economy (see Payments and Expanding the Global Economy).

3. Bespoke and Currated Needs

The demand for custom-made products, such as a specialized racing go-kart or a minivan modified for accessibility, and curated experiences frequently involves intricate processes of requirement gathering, iterative design, and the coordination of multiple suppliers or services. The global personalized gifts market serves as a strong proxy for this demand, projected to grow from $31.05 billion in 2025 to $43.5 billion by 2029, with a CAGR of 8.8%.20 Another estimate suggests a $10.76 billion expansion in this market between 2025 and 2029, at a CAGR of 6.7%.21 North America is a significant driver, accounting for 29% of this market and anticipated to be the fastest-growing region.20

Agentic AI can function as a sophisticated project manager or an intelligent personal shopper in this domain. These AI agents can understand nuanced user needs expressed in natural language, source appropriate components or services, facilitate complex design processes, and manage the end-to-end creation and delivery of bespoke products. This includes AI-driven personalization at scale for items that require substantial user input or co-creation, effectively translating vague consumer desires into concrete product specifications and managing multi-step production or assembly workflows. Platforms like “Daydream” allow users to ‘chat to shop’ for fashion using natural language prompts, receiving tailored suggestions rather than infinite options.

The role of the agent in the custom goods sector extends beyond simple matching to active co-creation and intricate problem-solving. This is particularly true for items that are not COTS and require a deep understanding design trade-offs, specialized components or modifications. AI stylists, for example, can curate entire outfits rather than just suggesting single items, and AI can facilitate hyper-personalized designs based on user descriptions. In this vein, customization can transition from a luxury niche to a more mainstream offering. A shift that could unlock significant latent demand from consumers who desire personalized products but are currently deterred by the cost or complexity of the customization process.

4. Circular Economy – Used Items and Loanable Goods

The market for used goods is experiencing rapid expansion, fueled by a combination of economic pressures compelling consumers to seek value and a growing societal emphasis on sustainability. The global resale market was valued at approximately $200 billion in 2023 and is projected to potentially double to $400 billion by 2027. Another analysis indicates the global second-hand products market stood at $186 billion in 2024 and is forecasted to surge to $1.044 trillion by 2035, reflecting a robust CAGR of 17.2% between 2025 and 2035. North America holds 40% share.

Agentic AI can significantly enhance marketplaces by improving the listing process through AI-generated descriptions and pricing suggestions derived from minimal inputs like a single photograph, facilitating more efficient discovery of specific items, aiding in the verification of authenticity or condition, and managing the intricacies of transactions or loan agreements. These functions directly address the inherent fragmentation, information asymmetry, and trust deficits prevalent in consumer-to-consumer (C2C) and business-to-consumer (B2C) used goods markets. Many consumers retain items they no longer need and refrain from selling due to the hassle of listing, pricing, shipping, or managing the exchange. By dramatically lowering the friction for suppliers platforms could bring millions of new items into circulation. This represents an expansion of the total addressable market of tradable used goods.

Platforms that cover this market also creates a distinction from Amazon, as they engage for a purpose. If the purpose is local, or used its ebay, craigslist or the AI platform. 

Table 1: Key US Edge Market Segments for Agentic Commerce

Market Making Challenges 

While edge opportunities are substantial, realizing them requires agentic platform operators to undertake significant operational and strategic work. For instance, aggregating and structuring a highly fragmented supply side, orchestrating a seamless and trustworthy end-to-end customer experience, and defining the governance (ex. return policy).

Challenge 1: Capturing and Structuring the Long Tail of Supply

A primary hurdle for agentic commerce platforms aiming to serve edge markets is the effective aggregation and organization of supply from a vast, diverse, and often technologically unsophisticated base of small and medium-sized businesses (SMBs) and individual providers.

1.1 Standardizing Diverse Product/Service Information for SMBs

To construct a functional marketplace, a structured and comprehensive catalog detailing what these myriad small businesses can offer, encompassing products, services, and real-time inventory availability. This necessitates the creation of robust schemas and data structures capable. Edge SMBs often lack people, and standardized for their own business. Or other specialist agents that can go through SMB purchase lists or online product listings. The task of onboarding and continuously updating data from millions of disparate SMBs is formidable and represents a usability and incentive challenge for 

The solution may lie in AI-driven data ingestion and structuring that minimizes the burden on the SMB, perhaps using Large Language Models (LLMs) to convert unstructured text into structured data attributes, or employing computer vision to extract product features from images. Incentives, such as immediate access to aggregated demand or premium visibility, will be crucial to encourage SMB participation and data sharing. Such a dataset would itself be a competitive barrier. My analogy is Alipay and Taobao as every small business is present in a single giant consumer market. 

1.2. Enabling Real-Time Inventory Visibility for Small Players

For many critical edge use cases, such as hyperlocal food delivery, local retail inventory access, and the exchange of used goods, real-time inventory data is key for customer experience and fulfillment. Given that SMBs lack sophisticated inventory management systems, Agentic platforms must furnish simple, intuitive tools or seamless integrations that empower SMBs to publish and update their inventory status with minimal effort. 

In a previous blog, I outlined how all small businesses publish their real time store inventory to Google. Why? Because when you search on outdoor furniture Google local shows “in stock at Crate and Barrell” with measurement to show conversions. Data sharing equals sales, this is the model that Agentic platforms must create.

The challenge is not just publishing inventory data; it involves maintaining its accuracy with minimal ongoing effort. Agentic AI systems could address this by learning sales velocities for specific items and proactively prompting updates (e.g., “It appears you may be running low on Product X. Do you still have 5 units in stock?”). Or perhaps integration with Block’s POS to  automatically update inventory upon a sale. 

If accomplished, and aggregator’s visibility across a distributed network of local SMBs could also unlock novel and highly efficient fulfillment models. For instance, an AI agent could orchestrate “hyperlocal Clicks-to-Bricks” fulfillment, where it locates an item at the nearest small store with confirmed availability and arranges for immediate customer pickup or rapid local delivery. Transforming isolated SMBs into a market of  interconnected nodes within a dynamic, responsive local supply network, orchestrated by the agentic platform.

Challenge 2: Orchestrating the CX

Beyond supply-side aggregation, agentic platforms face the equally daunting task of managing the entire customer journey, from initial discovery through to fulfillment and post-purchase support. This is particularly complex in decentralized markets populated by numerous small, independent suppliers.

2.1. From Discovery to Fulfillment and Post-Purchase Support

Agentic platforms must be capable of guiding users from engagement through the discovery of unique or highly specific items and services, to the delivery and navigate post-purchase scenarios such as returns, exchanges, or complaints. This end-to-end orchestration is challenging within a single domain and even more so in decentralized market with no governance. AI agents will need to coordinate complex logistics, potentially integrating with third-party delivery providers, manage customer expectations regarding timelines and quality, and ensure consistent service levels across a diverse supplier base. AI must also articulate the risk. Who owns it? Can I return my goods? 

The “orchestration” challenge implies the necessity for AI agents to collaborate with other specialized AI agents or automated systems, such as logistics APIs, payment gateways, customer service chatbots, and inventory management modules. Multi-agent system architectures enable agent specialization within a segment and geography.  While product discovery can be approximated by advanced search engines and payment processing is a relatively commoditized function, coordination represents a significantly harder problem to solve at scale.  Mastering this orchestration for the “everything else” economy would create unique value, defensible not merely through network effects but through profound operational and AI-driven sophistication.

2.2. Governance and Risk in Decentralized Networks

Ensuring consistent product quality, reliable service delivery, and the effective management of exceptions like returns or disputes presents a major hurdle.  Unlike integrated retailers such as Walmart, which explicitly stand behind every purchase and manage the associated risks, a decentralized agentic marketplace requires robust mechanisms to build and maintain trust when the platform operator neither owns the inventory nor directly controls the service provision. 

AI-driven solutions for supplier vetting, ongoing performance monitoring, dynamic reputation systems, secure escrow services, and transparent, integrated dispute resolution processes. Consumer trust in AI agents and the platforms they operate on will depend heavily on the existence of such assurance frameworks and clear recourse mechanisms.

Replicating the “Walmart trust factor” in a decentralized market requires platforms to evolve their governance into a trusted guarantor or mediator, even if it does not directly supply the goods or services. For example, AI can analyze supplier reviews, disputes, or historical performance data. As I related, Visa and Mastercard are best placed to define the governance structure of agentic operation across all platforms (a significant 5yr VAS opportunity). 

The Platform (or V/MA) becomes an enforcer of quality standards and a cornerstone of market trust. The data generated from managing quality, resolving disputes, and tracking supplier performance across a vast network of edge transactions can, in turn, be used to create highly accurate, dynamic “trust scores” for suppliers. These scores, far more nuanced than simple star ratings, become a valuable asset for the platform, a reliable signal for consumers, and a powerful incentive for suppliers to maintain high standards of conduct and service quality.

Challenge 3: Pricing, Risk, and Trust

Agentic platforms must not only connect buyers and sellers but also establish the fundamental rules and mechanisms that govern their interactions, particularly concerning pricing, risk allocation, and the overall framework of trust.

3.1. Pricing Mechanisms 

For the non-commodity goods and services pricing is often not a fixed affair. It can involve dynamic mechanisms such as auctions, formal Request for Proposal (RFP) processes, or bespoke, bilateral negotiations. The Michelin-Ford tire example, where an RFP that failed to specify “brand” led to an initial supply of unbranded tires, underscores the complexity of value assessment beyond simple price points in such transactions. Agentic AI platforms must be architected to support these complex pricing mechanisms.  This could involve AI agents capable of participating in, or even autonomously conducting, negotiations on behalf of buyers or sellers as agents take into account factors beyond price: product quality, delivery timelines, supplier reputation, warranty terms, and other non-monetary aspects of a deal.  Platforms like Keelvar demonstrate agentic AI autonomously designing and managing sourcing events, while Pactum shows AI agents engaging in autonomous supplier dialogue and agreement finalization. 

Dynamic, multi-attribute negotiations at scale is an upmarket that could be made available and usable by SMBs. This could lead to more efficient market clearing for unique items and services, where value is assessed holistically rather than on price alone. Sophisticated AI negotiating agents also raises the prospect of new forms of complex market dynamics, including the potential for “algorithmic collusion,” where AI agents, even without explicit programming to do so, learn pricing strategies that lead to supra-competitive outcomes.  Such developments would necessitate new regulatory frameworks and oversight mechanisms to ensure market fairness, particularly in markets for unique or essential custom goods and services.

3.2. Who is Responsible? Competing Against The “Walmart” Trust Factor

As highlighted previously, established retailers like Walmart cultivate consumer trust by explicitly backing every purchase and providing clear avenues for recourse. In a decentralized agentic marketplace, defining who bears responsibility for fulfillment, ensuring product or service quality, and impartially handling disputes is a critical and complex challenge.  To compete, platforms must establish market rules leverage AI for comprehensive risk assessment of transactions and suppliers, advanced fraud detection, this is also an area where V/MA can lead as well as facilitation of an efficient dispute resolution processes  Examples includ implementing platform-backed guarantees, secure escrow services managed via smart contracts, or specialized insurance mechanisms tailored for agent-mediated commerce. The development of “Agent Passports” or similar identity verification systems for AI agents can also contribute to building trust and accountability within these ecosystems. For end to end assurance a qualification of every participating agent and entity is necessary. This is the “market”.

Table Challenges and Approaches

Why Edge?

Edge use cases present a greater need for the sophisticated orchestration capabilities that AI and agentic commerce can provide. While the initial investment in terms of effort and resources to establish these markets is more substantial, the potential upside for a successful market maker aligns to their market valuations.

Edge markets offer what is effectively a “greenfield” opportunity. Here, new entrants can define the rules of engagement, build powerful network effects from the ground up, and capture significant economic value by bringing order and efficiency to previously chaotic or underserved segments of economic activity. In such contexts, the role of AI transcends incremental improvement, as might be seen in core retail applications, to become one of fundamental enablement and market creation.

The “greater need for AI orchestration” within these unstructured markets signifies that AI itself becomes a core, indispensable component of market creation, customer experience and the overall value proposition, not merely an ancillary optimization tool. 

Platforms that undertake the “hard work” to organize this supply and demand capture a proportionally larger share of the value created in each transaction. This is not a “new kind of search” that competes with Google and Amazon. It is not even eCommerce in the traditional sense. This is enabling a new market and way to coordinate it that only AI can deliver. By handling complex discovery, nuanced negotiation, intricate customization processes, and multi-party logistics coordination, the AI’s contribution to the final transaction value is substantially higher. This justifies a more significant revenue share for the platform that provides this essential orchestration layer.

The process of successfully organizing one complex edge market using agentic AI can lead to the development of a reusable “playbook.” This playbook, comprising refined AI models, standardized data schemas, proven trust mechanisms, and effective orchestration logic, can significantly accelerate entry into other, similarly unstructured markets. The “hard operational work” undertaken to structure supply, build robust trust frameworks, and orchestrate fulfillment for, say, custom artisan goods, involves creating specific AI capabilities and market rules. This framework can be reused. Reuse creates powerful economies of scale and scope in the process of market-making itself, allowing the platform to achieve compounding competitive advantages as it expands its footprint across the “everything else” economy.

Global Blueprint for Agentic

Assuming an agentic US success in hyperlocal, the resultant models, technologies, and operational learnings can serve as a valuable blueprint for expansion into smaller, often mobile-first, markets in emerging economies, such as those found in Africa and Latin America. These markets frequently exhibit highly fragmented local economies, a significant presence of informal sector activities, and a greater dependency on mobile platforms for commerce and communication. These characteristics make them potentially fertile ground for agentic commerce solutions tailored to organize local supply chains—for example, in agriculture, local retail, or the production of artisan goods—and connect them efficiently to existing and new sources of demand.19 This approach would mirror the successes in US hyperlocal markets but would be carefully adapted to the unique socio-economic, cultural, and technological contexts of these regions. The global agentic commerce market, including solutions across retail, payments, and customer service, was estimated between $5-6.7 billion in 2024 and is projected to reach $10.41 billion by the end of 2025, with forecasts suggesting it could touch $41-200 billion between 2030 and 2040, indicating substantial global growth potential.76

The potential for “leapfrogging” is particularly pronounced in many emerging markets. Analogous to how mobile telephony bypassed the extensive rollout of landline infrastructure in many developing nations, agentic commerce platforms—optimized for mobile-first interaction and adept at navigating the nuances of fragmented and informal economies—could circumvent the need for the development of traditional, Western-style retail infrastructure. Many emerging markets lack the highly organized retail chains, sophisticated logistics networks, and formalized business structures prevalent in the US.43 If agentic AI can effectively organize informal sellers, smallholder farmers, and local producers—who form the backbone of many economies in Africa and Latin America—and connect them to buyers primarily via mobile interfaces, it can catalyze a new paradigm of commerce that does not depend on massive prior investment in physical infrastructure.72

The deployment of agentic commerce in these emerging markets could also yield significant socio-economic benefits. By empowering small-scale entrepreneurs, local artisans, and agricultural producers with direct access to larger markets, transparent price discovery, efficient operational tools, and potentially integrated financial services (such as micro-payments or escrow facilities accessible via mobile), these platforms can substantially uplift local economies.75 This has the potential to foster more inclusive growth, improve livelihoods, and enhance the resilience of local communities. 

Success Lies in Aligning Value with Needs

The trajectory for agentic commerce to achieve significant economic impact and widespread adoption does not lie in pursuing incremental improvements within already optimized, mature markets. Instead, its most profound potential resides in the ambitious endeavor of structuring, organizing, and efficiently serving the vast, underserved “everything else” economy. By strategically focusing on edge use cases characterized by clear unmet needs agentic platforms can sidestep direct confrontation with entrenched incumbents. This approach allows them to concurrently build valuable, defensible networks and cultivate sophisticated AI-driven orchestration capabilities.

The sophisticated AI systems honed in these challenging environments, trained on diverse, real-world edge case data and refined through continuous interaction, will establish insurmountable competitive barriers. The established supplier relationships, the nuanced pricing and risk algorithms, and the innovative trust mechanisms will collectively constitute a powerful strategic asset. Ultimately, the economic opportunity extends beyond immediate transactional revenues; it encompasses the creation and ownership of new market infrastructures. The robust capabilities forged in addressing the complexities of US-based edge markets will provide a solid and adaptable foundation for strategic expansion into emerging economies, where the need for such organizational and efficiency-enhancing technologies is often even more acute. By making the long tail of supply accessible, manageable, and efficient, agentic commerce is poised to reshape the contours of commerce on a global scale, unlocking new avenues of value creation for businesses and consumers alike.