Agentic commerce: architectural implications for product and engineering teams

As the population becomes more and more entrenched in AI, the way consumers are shopping is seeing a fundamental shift.

Agentic commerce is changing the way digital transactions are initiated, evaluated and executed. While earlier waves of e-commerce focused on improving interfaces and conversion flows, agentic commerce moves decision-making authority into autonomous or semi-autonomous AI systems that act on behalf of users.

For CTOs, product leaders, and developers, this is not a marketing concept; it’s an architectural question. If software agents begin to discover products, compare suppliers, negotiate trade-offs and execute payments independently, commerce infrastructure must evolve accordingly.

This article explores how agentic commerce differs from traditional e-commerce and outlines the technical implications for teams responsible for building and operating large-scale commerce systems.

From user-driven flows to delegated execution

Traditional e-commerce is user-driven. The system exposes catalogue data, pricing and payment methods. The user browses, selects, confirms and completes the transaction. Even where machine learning is present, for example in recommendations or fraud detection, the user remains the primary decision-maker.

Agentic commerce changes this control surface. According to Salesforce, agentic commerce enables AI agents to reason, plan and take action across the commerce lifecycle rather than responding to isolated prompts or optimising single steps such as search ranking. In practical terms, this means that agents can be authorised to execute transactions when predefined conditions are met.

Mastercard describes this shift as moving from click-based to intent-based commerce, where systems interpret user goals and operate within bounded parameters. The key distinction is that the agent does not simply assist the user. It can act autonomously once given permission.

For product and engineering teams, this reframes the commerce journey: Instead of designing linear flows optimised for human navigation, teams must design programmable interfaces that agents can interrogate, reason over and transact through.

What makes a commerce system “agent-ready”?

Agentic commerce requires several architectural capabilities that go beyond traditional API exposure.

First, systems must support machine-readable product, pricing and availability data with sufficient semantic structure for agents to compare alternatives. McKinsey highlights that agents will evaluate offers across multiple dimensions, including price, delivery windows, sustainability attributes and loyalty incentives. This requires structured data models and consistent metadata rather than presentation-layer abstractions.

Second, execution pathways must be deterministic and permission-aware. An agent acting on behalf of a user must be able to initiate a transaction without interactive friction, provided it operates within authorised constraints. IBM notes that agentic commerce depends on clear rules, policies and governance frameworks that define what an agent can and cannot do. This implies programmable authorisation layers and fine-grained access controls.

Third, payment infrastructure must support variable and delegated transactions. JP Morgan argues that agent-driven commerce will require payment systems capable of handling dynamic pricing, recurring adjustments and pre-approved spending envelopes. Traditional one-off card authorisations are insufficient when agents are expected to manage replenishment or switch suppliers autonomously.

These requirements converge around a common theme: commerce platforms must become more composable, observable and policy-driven.

The role of APIs and developer tooling

For developers, agentic commerce increases the importance of well-designed APIs. Mastercard’s developer documentation on agentic commerce emphasises that agents require secure, authenticated and auditable interfaces for offer discovery, transaction initiation and post-transaction reconciliation.

This has several implications.

Authentication flows must distinguish between human and agent identities while maintaining traceability. Logging and audit trails become more critical, as decisions are executed autonomously. Rate limits and throttling strategies must account for agent behaviour patterns, which may differ significantly from human interaction patterns.

Google Cloud frames agentic retail as a coordination problem across systems, where AI agents orchestrate inventory, pricing and fulfilment decisions in near real time. From an engineering perspective, this places additional demands on system reliability and latency. Agents will not tolerate brittle integrations or inconsistent state across services.

In short, the quality of developer experience becomes a competitive factor. Platforms that expose consistent, well-documented and policy-aware APIs will be easier for agents to integrate with and reason over.

Product design in an agent-mediated environment

Agentic commerce introduces a new class of “user”: the AI agent itself.

Ad Age has argued that brands must ensure their value propositions are legible not only to consumers but also to the agents acting on their behalf. Translating this into product terms means designing offers, incentives and loyalty programmes that are machine-interpretable.

For example, discount logic that is opaque or dependent on manual coupon entry will be harder for agents to optimise. Conversely, structured, rule-based incentives can be programmatically evaluated and prioritised.

This shift also affects experimentation. In traditional commerce, A/B testing focuses on human behaviour at interface touchpoints. In agentic commerce, experimentation may extend to API responses, pricing transparency and machine-readable trust signals. Product metrics must account for agent-driven sessions as distinct from human sessions, with different engagement and conversion patterns.

The implication is that product strategy must expand beyond user interface design to include data model design and machine-facing experience design.

Payments and risk in agentic commerce

Payments are central to agentic commerce because they formalise delegated authority.

If an agent is authorised to spend within a defined budget or to execute purchases based on price thresholds, the payment layer must enforce those constraints reliably. This aligns with Mastercard’s observation that security, transparency and user control will determine the pace of agentic adoption.

This means evaluating whether existing payment integrations can support programmable limits, real-time revocation and granular reporting. Fraud models may also need to adapt. Behavioural patterns of agents differ from those of humans, which may affect anomaly detection baselines.

JP Morgan highlights that financial institutions and merchants will need new frameworks to define liability and dispute resolution when transactions are agent-initiated. Engineering teams must ensure that transaction metadata clearly indicates agent involvement, preserving auditability and compliance.

Why this shift represents a material opportunity

The opportunity in agentic commerce is not limited to incremental efficiency gains. McKinsey suggests that AI agents could reshape competitive dynamics by lowering search and switching costs, compressing margins for undifferentiated suppliers while rewarding those with structured, transparent and high-performing systems.

For enterprises with robust infrastructure, this presents an advantage. Systems that are modular, API-first and policy-driven are better positioned to integrate with emerging agent ecosystems. Conversely, monolithic or interface-bound architectures may struggle to participate.

Salesforce frames agentic commerce as a new operating model for digital commerce rather than a feature layer. From a technical standpoint, this means that investment decisions made today around composability, observability and programmable controls will influence future competitiveness.

Preparing your stack for agentic commerce

Agentic commerce will not replace traditional e-commerce overnight. Human-driven journeys will continue to coexist with agent-mediated flows. However, the direction of travel is clear.

Engineering and product leaders should evaluate:

• Whether core commerce data is sufficiently structured and machine-readable
• Whether APIs support secure, policy-aware transaction execution
• Whether payment systems can handle delegated, variable authorisations
• Whether logging and audit frameworks can distinguish and trace agent activity
  

These are not speculative questions. Rather, they are architectural considerations that align with broader trends in AI integration and automation.

Conclusion

Agentic commerce represents a transition from interaction-driven to policy-driven commerce. It requires systems that are composable, observable and built around programmable intent.

Organisations that prepare their stacks to support agent-mediated execution will be better positioned as this model matures. Those that continue to optimise only for human interface flows may find themselves constrained when autonomous agents become a primary channel of demand.

Frequently asked questions

What is agentic commerce in technical terms?

‍Agentic commerce refers to systems where AI agents are authorised to discover, evaluate and execute transactions on behalf of users within predefined rules and constraints.

How does agentic commerce affect API design?

APIs must be machine-readable, policy-aware and auditable, enabling secure transaction execution without human interaction at every step.

Why do payments need to evolve for agentic commerce?

Agents require programmable authorisations, variable spending limits and real-time revocation capabilities to operate safely and within user-defined boundaries.

Is agentic commerce replacing traditional e-commerce?

No. It will coexist with traditional flows, but it introduces new architectural requirements that forward-looking enterprises should address now.

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