AgentPMT - The Agentic Economy
Marketplace Design for Agents: When the Buyer Is Software

Marketplace Design for Agents: When the Buyer Is Software

By Stephanie GoodmanDecember 16, 2025

When your marketplace's most important customer can't see a screenshot or read a testimonial, everything about how you design listings, discovery, trust, and transactions has to change.

Successfully Implementing AI AgentsMCPautonomous agentsAI Agents In BusinessAgentPMTDynamicMCPAI MCP Tool ManagementEnterprise AI ImplementationAgentic Payment Systems

A traditional marketplace listing includes a hero image, a catchy tagline, a wall of feature bullets, and a five-star rating. It works because the buyer has eyes, opinions, and an afternoon to comparison-shop. Now replace that buyer with an autonomous agent running a procurement workflow at 3 a.m. The hero image is irrelevant. The tagline is noise. The five-star rating is a floating-point number with no grounding in the agent's actual selection criteria. And that afternoon of comparison shopping needs to happen in under 200 milliseconds.

This is not a hypothetical scenario. Salesforce is scaling its Agentforce platform toward a billion deployed agents, with 93% of enterprise IT leaders reporting they have implemented or plan to implement AI agents within two years. Microsoft launched Entra Agent ID to give agents discoverable identities and metadata schemas. The infrastructure for agents to find, evaluate, and pay for tools is being built right now. The question is whether the marketplaces serving those agents are designed for their actual needs -- or are still optimized for a human who will never show up.

Platforms like AgentPMT are already building for this shift, treating the marketplace itself as agent-native infrastructure -- where listings, discovery, trust, and transactions are designed for software consumers from the ground up. This article examines what has to change about each of those layers when your primary buyer is an autonomous agent.

Schema-First Listings: The End of Marketing Copy as Interface

Human-facing marketplaces are built around persuasion. Agent-facing marketplaces are built around contracts.

As PYMNTS reported, AI agents evaluate products through "structured data quality, machine-readable policies, endpoint reliability, and fulfillment performance" rather than brand narratives or emotional design. The implication is blunt: "If the agent does not recommend it, the human never encounters it." A tool with brilliant marketing copy but incomplete metadata is invisible to the fastest-growing buyer segment on the internet.

What does a listing actually need to contain when the buyer is software? At minimum: a machine-readable schema declaring inputs, outputs, side effects, error codes, pricing unit, and data sharing policies. Not a description of those things -- the actual structured data itself.

Consider the difference. A human-facing listing might say: "Our enrichment API returns company data including firmographics, technographics, and contact information. Pricing starts at $0.02 per request." An agent needs something closer to this:

{
  "name": "vendor.enrich_company",
  "version": "2.1.0",
  "pricing": { "unit": "request", "price_usd": 0.02 },
  "side_effects": "none",
  "data_shared": ["company_domain"],
  "inputs": { "domain": "string" },
  "outputs": { "firmographics": "object", "technographics": "object" },
  "retriable_errors": ["rate_limited", "timeout"],
  "terminal_errors": ["invalid_domain", "not_found"],
  "sla": { "p99_latency_ms": 800, "uptime_percent": 99.9 }
}

The prose description is not wrong. It is just not the primary interface anymore. Salesforce's metadata team calls this the abstraction principle: metadata-driven design "allows applications to evolve without constant code rewrites" by establishing machine-readable structure as the foundation layer, with human-readable labels layered on top. The marketplace that gets this order backwards -- prose first, schema maybe -- will lose to the one that gets it right. AgentPMT's vendor listing system reflects this principle: tool providers publish structured schemas with declared inputs, outputs, and per-tool pricing, making every listing machine-queryable from day one.

This is what "schema-first listing design" means in practice. The structured data is the product listing. Everything else is supplementary documentation for the humans who maintain and configure the agents.

Discovery When the Shopper Cannot Browse

Humans discover tools by browsing categories, reading reviews, scanning screenshots, and following recommendations from colleagues. Agents discover tools by querying capability descriptions against task requirements. These are fundamentally different information retrieval problems, and most existing marketplace UX patterns are useless for the second one.

The token economics alone make traditional approaches unworkable. Speakeasy's research on dynamic MCP toolsets quantified the problem precisely: a static marketplace with 400 tools consumes over 405,000 tokens just to present the catalog to an agent -- more than double Claude's entire context window. The tools cannot even be listed, let alone evaluated. For 40 tools, the cost is still 43,300 tokens before the agent processes a single query.

Two approaches are emerging. Progressive discovery uses hierarchical navigation -- the agent sees categories first, drills into subcategories, then retrieves full schemas for specific tools. Semantic search uses vector embeddings to match a natural language capability description against pre-embedded tool descriptions, returning only the best matches. Speakeasy's benchmarks show semantic search consuming roughly 1,300 tokens initially regardless of catalog size, compared to 1,600-2,500 for progressive discovery. Both achieve near-100% success rates and represent a 100x reduction compared to static approaches.

The practical implication: an agent marketplace needs a discovery API where agents describe what they need -- "enrich company data from a domain, under $0.05 per call, no side effects, returns firmographics" -- and the marketplace returns ranked matches based on capability overlap, constraint satisfaction, and trust signals. Keyword search is a fallback, not the primary interface. AgentPMT's DynamicMCP marketplace addresses this directly: rather than pre-loading an entire tool catalog, agents discover and fetch tools on-demand through a single integration point, eliminating the token bloat that makes static catalogs unworkable.

Microsoft's Agent Registry adds another layer: discovery governance. When an agent queries the registry, the system evaluates the querying agent's metadata and permissions before returning results. Not every agent gets to see every tool. This matters for enterprise deployments where data sensitivity and vendor approval workflows constrain what an agent should even know exists.

The MCP Discovery project, with over 14,000 MCP servers indexed and a machine-to-machine API designed for programmatic discovery, tells you where tool distribution is heading. The directory is being built for software consumers first.

Trust Signals for Software Buyers

Humans trust star ratings, review counts, brand recognition, and the general vibe of a well-designed landing page. None of these signals are computable in a way that helps an agent make a procurement decision under constraints.

An agent evaluating a tool needs to answer specific questions: Will this tool respond within my latency budget? Will it be available when I need it? Does its schema match what I expect? Has its behavior changed since the last time I used it? Will it cost what it says it costs? These are engineering questions, not sentiment questions.

The trust signals that work for software buyers are structural. Verified schemas mean the tool's declared inputs and outputs have been validated against actual behavior -- contract testing, essentially. Cryptographically verifiable SLAs are emerging; researchers have demonstrated frameworks for verifiable SLA claims using trusted hardware monitors and zero-knowledge proofs, scaling to over one million events per hour. Microsoft's Trust Imprint Protocol creates verifiable provenance records of an agent's identity and behavioral history that downstream services can check before accepting high-risk actions.

What about reviews? They are not useless, but they need to be reformulated. A human review saying "Great API, fast response times, friendly support team" is meaningless to an agent. An automated quality signal saying "99.7% schema conformance over 50,000 calls in the last 30 days, with a p95 latency of 340ms and zero unexpected side effects" is actionable. The marketplace's job is to collect, verify, and expose this kind of telemetry -- not aggregate opinions. AgentPMT's audit trail infrastructure supports this shift: every tool invocation is logged with cost, latency, and outcome data, building the kind of structured performance history that agents can evaluate programmatically.

This is where the marketplace platform earns its fee. Any directory can list tools. A marketplace that runs continuous contract tests against its listed tools, collects real performance telemetry from actual agent usage, and exposes those signals through structured metadata creates a trust layer that individual providers cannot replicate on their own. The platform becomes a verification authority, not just a listing service.

Marketplace Mechanics at Micro-Scale

When a human buys software, the transaction is large, infrequent, and negotiated. An annual SaaS contract for $10,000 involves sales calls, procurement reviews, and a signature. When an agent buys tool access, the transaction is small, frequent, and programmatic. A data enrichment call for $0.02, happening five thousand times per day across dozens of agent workflows, is a fundamentally different economic motion.

Traditional payment infrastructure was not designed for this. Credit card processing fees alone make sub-dollar transactions economically irrational. Invoice-based billing requires a human to approve and reconcile. Subscription models force agents to commit to tools they might only need intermittently.

This is why protocol-level payment is becoming critical infrastructure. The x402 protocol, developed by Coinbase and supported by a foundation co-launched with Cloudflare, embeds payment directly into HTTP requests. An agent calls a tool, includes a payment authorization in the request header, and the tool executes. No invoices. No approval queues. No human required. Since launching in mid-2025, x402 has reportedly processed over 100 million payments across APIs and AI services, with Cloudflare additionally proposing a deferred payment scheme for transactions that do not require immediate settlement.

The marketplace implications are significant. Per-call billing with sub-cent granularity means agents can select tools dynamically based on real-time cost constraints -- the same enrichment query might route to a cheaper provider during a budget-constrained batch run and to a faster, more expensive provider during a latency-sensitive workflow. The marketplace becomes a real-time matching engine between agent requirements and provider capabilities, with price as one dimension alongside latency, accuracy, and trust scores. AgentPMT's DynamicMCP integration already operates on this model: tools are fetched on-demand, charged per use via per-tool pricing and x402Direct payments, and the marketplace handles discovery and payment in a single flow without requiring agents to pre-load catalogs or maintain per-provider accounts. Budget controls enforce spending limits at the transaction level, ensuring agents cannot exceed their allocated thresholds.

The billing infrastructure itself needs to be agent-grade. Usage reporting must be real-time, not monthly. Dispute resolution needs to be automated for common cases -- duplicate charges, error-but-billed calls, SLA violations. Budget enforcement needs to happen at the transaction level. If an agent is capped at $50 per day and has spent $49.98, the marketplace needs to know that before authorizing the next call, not on the invoice next month.

The Two-Sided Problem: Bootstrapping an Agent Marketplace

Every marketplace faces the cold-start problem: you need sellers to attract buyers, and buyers to attract sellers. Agent marketplaces face a specific variant of this that is both harder and easier than the traditional version.

Harder, because the buyer side requires technical integration. A human can browse a marketplace on a whim. An agent needs an MCP connection, a discovery API integration, payment rails, and policy configuration before it can make its first transaction. The friction of onboarding an agent consumer is orders of magnitude higher than onboarding a human browser.

Easier, because once an agent is connected, it does not comparison-shop out of boredom or switch providers because of a marketing campaign. Agent consumption is programmatic and habitual. An agent that discovers a reliable tool at the right price point will call it thousands of times without re-evaluating the decision until a constraint changes. The retention dynamics are stickier than human marketplaces, where brand loyalty is always one competitor's discount away from collapsing.

Marshall Van Alstyne, co-chair of the 2025 MIT Platform Strategy Summit, identified the core challenge directly: "You are going to launch a marketplace, you're going to have to onboard and get critical mass for agents." He further raised the governance question that makes agent marketplaces structurally different from human ones: "What happens when agent decision ability exceeds its formal authority?" In a human marketplace, a buyer who overspends gets a credit card bill. In an agent marketplace, a buyer that exceeds its authority might execute thousands of transactions before anyone notices.

Network effects in agent marketplaces follow a different curve. Adding another tool provider increases the probability that an agent's capability query returns a match, which increases overall utility, which attracts more agents, which attracts more providers. Research from Monetizely suggests platforms with strong network effects achieve price premiums 30-50% higher than those without. The critical insight: breadth of tool coverage matters more than depth in any single category during the bootstrapping phase. An agent that can find seven out of ten tools it needs on your marketplace will connect. One that can find three will not bother.

The practical strategy: start with tool providers. Make listing frictionless -- if a provider has an OpenAPI spec or MCP server, onboarding should be a configuration step, not a development project. Speakeasy's work on generating MCP tools from OpenAPI specs points in this direction. On the agent side, reduce integration to a single connection point. One integration, entire marketplace, zero incremental activation cost -- the design philosophy behind approaches like AgentPMT's cross-platform DynamicMCP, which provides compatible access across Claude, ChatGPT, and other agent frameworks through a single marketplace connection.

Implications for Agent Infrastructure

The shift from human buyers to software buyers does not just change marketplace UX -- it restructures the entire value chain around agent commerce. Three implications stand out.

First, marketplace platforms become infrastructure, not storefronts. When every transaction is programmatic, the marketplace's value is not in presenting options attractively but in executing discovery, trust verification, and payment settlement reliably at machine speed. Downtime is not a bad customer experience; it is a broken workflow. The SLA expectations for the marketplace itself now mirror those of the tools it hosts.

Second, the competitive moat shifts from catalog size to trust data. Any platform can list tools. The platform that accumulates the richest telemetry -- verified schema conformance rates, real-time latency distributions, cost accuracy scores, side-effect audits -- creates a trust layer that agents depend on and that competing platforms cannot replicate without equivalent transaction volume. This is a data network effect layered on top of the traditional marketplace network effect.

Third, governance becomes a product feature, not a policy document. Budget controls, authority boundaries, discovery permissions, and audit trails are not compliance overhead -- they are the features that enterprise buyers evaluate when deciding whether to connect their agents to a marketplace. The platform that treats governance as first-class infrastructure will capture the enterprise segment, where the highest-value agent workflows operate under the strictest constraints.

What to Watch

Three convergence points will shape how agent marketplaces evolve over the next twelve to eighteen months.

First, metadata standardization. Microsoft's Agent Registry, Salesforce's metadata framework, and the MCP specification are all moving toward structured agent and tool metadata as a foundational layer. When these schemas converge -- or when a dominant standard emerges -- listing portability across marketplaces becomes possible, and the competitive axis shifts from catalog lock-in to trust quality and transaction cost.

Second, payment protocol maturity. The x402 Foundation, backed by Coinbase and Cloudflare, is pushing toward broader adoption of HTTP-native payments. As deferred payment schemes and multi-chain settlement mature, the friction of per-call billing drops further, making micro-transactions genuinely viable at scale. Watch for payment protocols that support agent-initiated dispute resolution, not just agent-initiated payments.

Third, discovery governance. The question of who gets to discover what -- and under what policies -- is still largely unsolved in open marketplaces. Microsoft's collection-based discovery governance is one model. Expect more experimentation with tiered discovery, where free tools are universally visible but premium or sensitive tools require policy-gated access. The marketplace that figures out discovery governance without strangling the network effect will have a structural advantage.

Key Takeaways

  • Schema-first listing design is not optional. When your buyer is software, structured metadata -- inputs, outputs, side effects, pricing, SLAs -- is the product listing. Marketing copy is supplementary documentation for the humans who configure the agents.
  • Discovery must be capability-driven, not keyword-driven. Semantic search and progressive discovery approaches reduce token consumption by 100x compared to static catalogs, making large-scale agent marketplaces technically viable. The marketplace needs a discovery API, not a browse interface.
  • Trust signals must be computable. Star ratings and review counts do not help an agent make a procurement decision. Verified schema conformance, real-time uptime telemetry, and contract test results create a trust layer that agents can actually evaluate programmatically.

Ready to build for the agent economy? AgentPMT provides the DynamicMCP marketplace, per-tool pricing, budget controls, and audit trails that agent-native commerce demands. Explore AgentPMT today to connect your agents to the tools they need.

Sources

Marketplace Design for Agents: When the Buyer Is Software | AgentPMT