Your Payments Product Is Designed for the Wrong Decision-Maker
- Kyle Tyacke
- May 28
- 10 min read
Kyle Tyacke, Director of Technology
Table of Contents
Agentic payments are transactions initiated and executed by AI agents acting under user or business policy — not direct human clicks. That distinction is small in description and enormous in consequence. For product managers and product marketers in the payments space, it signals a shift that touches every layer of your roadmap: how value is created, where trust lives in the stack, who you're selling to, and which metrics prove you're winning.
According to Accenture's Future of Money research, 57% of executives believe agentic payments will become mainstream within the next three years. The infrastructure race is already underway. Visa has completed hundreds of agent-initiated transactions in live production environments, with AI agents interpreting user-defined rules, authenticating authorization, and completing purchases. If your product and go-to-market (GTM) motion still centers on checkout conversion, you're optimizing for a moment that's being engineered out of the flow.
This is a field guide for product and marketing teams building in the agentic payments world. It covers the five shifts that matter most: mandates, orchestration, trust, developer experience (DX), and business model design.
Mandates Replace the Checkout Click
The most fundamental change in agentic payments is where human intent gets expressed. In a conventional flow, intent surfaces at checkout: the user reviews a cart, selects a payment method, and clicks confirm. In an agentic flow, intent is expressed once, upstream, as a structured mandate, and the agent handles execution repeatedly from there.
A mandate is a structured authorization object that defines scope: how much an agent can spend, with which counterparties, across which rails, within which time window. A policy adds optimization logic on top: minimize FX cost, pay suppliers within X days, stay within this category budget. Together, these objects replace the checkout click as the primary decision surface for the user.
This has real implications for how you build and market.
The customer journey compresses: The traditional three-stage path (browse, decide, pay) becomes: define policy once, then let the agent run. Most of the human touchpoints your product currently supports are disappearing or moving upstream into mandate-creation flows and ongoing dashboards.
The value proposition changes language: "Frictionless checkout" was the dominant promise for a decade of payments product marketing. The agentic equivalent is closer to: "safe, intelligent, ongoing execution aligned with your policies." That's a harder sentence to fit on a landing page, but it's the accurate one. Teams that keep leading with checkout language will misposition themselves as the market matures.
"The agentic mandate isn't a form field. It's the product."
Where to start: Audit your current product surface for where mandates and policies would live as first-class data structures. If they don't exist yet, that's the product gap to prioritize, not another checkout flow optimization.
For more on how buyer expectations shift when AI enters the transaction chain, read When Your Agent Gets the Transaction Wrong, Who Pays?, Catchy's breakdown of the accountability infrastructure the agentic economy demands.
Agents Are Now the Orchestration Layer
In a conventional payments stack, routing decisions live in static gateway logic: if card type is X and amount is under Y, route to acquirer Z. Agentic architectures replace that static table with dynamic, context-aware decision-making at the agent level.
AWS describes this role as the "Cognitive Payments Director", an agent that orchestrates across payment service providers (PSPs), acquirers, networks, and rails, making per-transaction decisions based on real-time signals: cost, foreign exchange (FX) rates, historical approval rates, fraud risk, and policy constraints.
This matters for product teams because the competitive surface is shifting. It used to live in checkout UX, conversion optimization, and integration breadth. In an agentic world, it increasingly lives in how "agent-legible" and "agent-controllable" your product is.
What "Agent-Legible" Actually Means
An agent-legible product is one an AI can reason about reliably. That means:
Signals are exposed via API. Approval rate history, real-time fee structures, FX spreads, risk scores, these need to be queryable. If an agent can't access them, it can't optimize across them.
Controls are programmable. Policy objects, spending limits, counterparty preferences, and category constraints need to be manageable by code, not just by human administrators in a UI.
Documentation is unambiguous. Large language model (LLM)-based agents parse your developer documentation to understand how to call your APIs. If your docs are vague, poorly structured, or rely on implied context, agents will misuse your product, and developers will go elsewhere.
The agent-legibility gap is already measurable. According to McKinsey's agentic commerce research, AI agents can now autonomously execute tasks that require more than 30 hours of human effort, and that capability threshold doubles approximately every 7 months. The products that give agents clean signals and programmable controls today will compound that advantage as agent capabilities scale.
Three deployment patterns are emerging in practice. Merchant-side agents optimize outgoing and incoming payments within merchant platforms. Bank or network-hosted agents are offered as services to corporate clients or embedded in consumer apps. Independent orchestration agents operate as third-party layers over multiple PSPs and banks. Each model creates different product integration requirements and different GTM entry points.
Trust and Identity Have to Move Into the Product Surface
This is the shift most payments product teams are underestimating. In a classic payments context, trust questions center on the human: is this user who they say they are, and did they authorize this transaction? In an agentic context, a new layer of questions appears before you can even get to the human.
Is this agent legitimately bound to the principal it claims to represent? Is it operating within the mandate it was given? Is the behavior consistent with prior activity at this agent-mandate level, not just at the card or account level?
Agentic AI can simulate thousands of fraud scenarios per second, dynamically adapting authorization thresholds without interrupting legitimate flows. That capability cuts both ways. Agents can be used to optimize fraud as effectively as they optimize legitimate payments. Fraud models built only on user-level signals will miss the agent-level attack surface entirely.
The Trust Primitives That Become Product Features
Research in this space identifies several trust primitives that move from compliance requirement to product surface in an agentic world:
Agent identity binding: a verifiable link between an agent and the principal (business or consumer) it's authorized to represent
Mandate verification: per-transaction confirmation that the payment falls within the agent's authorized scope
Consent capture and audit log: a structured record of what was authorized, when, and under which policy terms
These aren't just infrastructure. They're differentiators. A product that exposes mandate management as a service, or offers an agent identity registry, creates a new revenue surface that didn't exist before. The positioning opportunity for product marketing is to move the narrative from "fraud prevention" to "trust infrastructure for agentic commerce," a meaningfully different framing that speaks to the CFOs, treasury leaders, and heads of AI automation who are now joining the buying conversation.
Key stat: Only 29% of UK consumers and 16% of US consumers currently trust AI to make payments on their behalf, underscoring the need for trust infrastructure as a product differentiator, not just a compliance checkbox. (Source: Infosys, via nevermined.ai)
For a deeper look at how the major networks are approaching agent identity and the accountability gap this creates for the broader developer tools ecosystem, read the blog 'When Your Agent Gets the Transaction Wrong, Who Pays?'.
Developer Experience Has Two Audiences Now
Payments products have always needed strong developer experience. The agentic shift adds a second audience to that design challenge, one that's non-human.
Your APIs, schemas, and webhooks are now being consumed by both human developers building applications and AI agents that will call them autonomously at runtime. That's a design constraint most documentation and SDK teams haven't fully internalized yet.
According to the Stack Overflow 2025 Developer Survey, 70% of developers who use AI agents report reduced time on specific development tasks, with 69% citing increased productivity. That means an agent evaluating your product today can assess far more competing options in the same window of time a human developer would previously have spent on one. Your DX has to work for both audiences or you risk being filtered out before a human ever reviews you.
Designing for Human Developers
The fundamentals here are familiar territory: clear API reference docs, well-maintained SDKs, sandbox environments with realistic test data, and code samples that reflect actual production patterns. For a practitioner's take on how documentation strategy is evolving for this dual-audience reality, see the blog from Catchy 'The Future of Documentation Is Machine-First'.
Designing for AI Agents
Agent-oriented DX adds a new layer of requirements. LLM-based agents infer how to call your APIs by reading your documentation and schemas. Ambiguous field names, inconsistent error codes, or documentation that relies on unstated conventions will cause agents to fail (often silently, in ways that are hard to debug).
Concrete design principles for agent-oriented DX:
Model mandates and policies as first-class objects, not as fields buried in transaction payloads.
Make webhooks diagnostic: return structured reasons for outcomes (approved, rerouted, blocked by policy) so agents can adapt their behavior.
Publish machine-readable schemas: OpenAPI specs and JSON Schema definitions should be current and complete, not supplementary.
Build agent simulation environments: developers and businesses need to test how agent behavior interacts with their policies before going live.
The GTM opportunity here is the phrase "agent-ready." When it's backed by concrete SDK capabilities, simulation tooling, and documented agent integration patterns, "agent-ready" becomes a verifiable product promise rather than a marketing claim. That distinction matters for the enterprise buyers making platform selection decisions right now.
For a breakdown of how agent-oriented evaluation is already reshaping the developer buying journey, read AI Agents Are Your Next Buyer Persona.
Your Metrics, Packaging, and Buyers Need a Reset
Here's the honest check for product and marketing leaders: if your north-star metrics and your packaging still reflect a checkout-centric model, you are measuring and selling the wrong thing.
Metrics That No Longer Tell the Full Story
Traditional payments success metrics (checkout conversion rate, cart abandonment, transaction volume, click-through rates on checkout UX) are designed to measure human-initiated flows. In an agentic world, a growing share of transactions bypasses those metrics entirely. An agent executing 10,000 supplier payments per month against a mandate will never appear in your checkout funnel data.
The metrics that matter in an agentic context:
Mandate adoption and utilization: how many policies have been created, and what share of eligible transactions are flowing through them
Agent-driven volume share: what percentage of total volume is agent-initiated versus human-initiated
Optimization outcomes: cost savings realized, approval rate improvements, FX spread reductions attributable to agent routing decisions
Policy adherence and override frequency: how often agents operate within mandate, and how often humans intervene
Key stat: McKinsey estimates the global agentic commerce market could reach $3–5 trillion by 2030, with the US B2C segment alone representing up to $1 trillion in orchestrated agent revenue. The question for product teams is whether your metrics framework would capture agent-driven volume at all.
Packaging and Pricing
Pricing structured around raw transaction counts will increasingly undervalue agentic capabilities. The providers who build durable commercial models will price based on the value delivered by the agent layer: mandates under management, active agent count, optimization outcomes, and volume moving through agent-driven routes.
Consider dedicated SKUs or add-on tiers for agentic capabilities (orchestration, risk monitoring at the agent-mandate level, mandate management tooling). These create a clear commercial signal in the market and establish a category before the field gets crowded.
Who You're Actually Selling To
The buyer persona shift may be the most underestimated dimension of this transition. The primary buyer for your agentic payments capabilities is not the checkout product owner or the head of e-commerce. It's the head of AI, the VP of Automation, the treasury leader deploying working-capital agents, or the platform architect building an internal AI system that needs to move money.
These buyers have different evaluation criteria, different success metrics, and different definitions of trust. Your messaging, your sales collateral, and your developer documentation all need to reflect that shift, not just in tone but in the specific problems they solve.
For a broader look at how complex enterprise buying committees are reshaping developer GTM, read the Catchy blog 'The Enterprise Developer Journey Isn't Linear Anymore. It's Orchestrated'.
Building for the Agent Layer, Not Around It
The payments teams that will struggle in an agentic world are those treating it as an extension of the checkout era: the same product logic, the same messaging, the same success metrics, just with an AI layer added on top. That framing misreads the shift.
The mandate, not the checkout, is the new design center. The agent, not the human, is the primary runtime consumer of your APIs. The trust infrastructure you expose is a product feature, not just a compliance obligation. And the buyers now making platform decisions are as likely to be AI platform architects as payments product owners.
Getting that product surface right and marketing it with specificity is the work ahead. Teams that move early on mandate design, agent-oriented DX, and buyer persona alignment will define the category before it consolidates.
Want to work through how your developer-facing product and messaging need to evolve for the agentic era? Talk to a Catchy strategist →
Frequently Asked Questions
What are agentic payments?
Agentic payments are transactions initiated and executed by AI agents acting under structured policies and mandates defined by users or businesses, rather than triggered by direct human action at checkout. The agent interprets goals (minimize cost, pay within terms, stay within category budget) and selects rails, providers, and timing accordingly. This is an intelligence layer above existing payment infrastructure, not a new rail.
How do agentic payments differ from automated or recurring payments?
Traditional automated payments (subscriptions, direct debits) follow fixed, pre-programmed schedules. Agentic payments are adaptive: the agent evaluates real-time context — FX rates, approval probability, cash position, policy constraints — and makes dynamic routing and timing decisions. The difference is between a scheduled instruction and a goal-directed optimization running continuously within defined limits.
What is a payment mandate in the context of agentic AI?
A payment mandate is a structured authorization object that defines the scope within which an AI agent can move money: amount caps, permitted counterparties, approved categories, time windows, and optimization preferences. Mandates replace the individual checkout confirmation as the primary consent surface, and are designed to authorize many transactions without requiring repeated human approval.
What does "agent-legible" mean for a payments product?
Agent-legibility describes how effectively an AI agent can understand, consume, and reason about a payments product's APIs and documentation. An agent-legible product exposes performance signals (approval rates, fees, risk scores) via API, models policies as first-class objects, and maintains unambiguous, machine-readable documentation. The more agent-legible a product is, the more reliably agents can use it, and the stronger its position in agent-driven orchestration architectures.
How should product teams rethink fraud and risk for agentic payments?
Fraud models built on user-level signals (is this account acting normally?) need a new layer for agent-level signals: is this agent operating within its mandate, and is its behavior consistent with prior activity at the agent-mandate level? New trust primitives including agent identity binding, mandate verification, and per-transaction policy enforcement become core risk features, not just compliance requirements.
Which teams should own agentic payments strategy inside a technology company?
Agentic payments strategy spans product management, product marketing, developer experience, and risk. But the buyers and champions inside enterprise customers are shifting: heads of AI, automation leads, and treasury functions are increasingly driving evaluation alongside traditional payments product owners. GTM teams that align their messaging, documentation, and sales engagement to this expanded buyer set will have a meaningful advantage in competitive cycles.
