Definition
Business to Agent to Consumer (B2A2C) is a go-to-market and transaction model where a business markets, negotiates, and/or sells through machine intermediaries (AI agents, bots, connected devices, or automated workflows) that interact with the end consumer (or the consumer’s own AI agent acting on their behalf).3
In the agentic-commerce variant you described, B2A2C typically becomes agent-to-agent commerce: a brand’s “seller agent” exposes products, terms, and fulfillment actions to a consumer’s “buyer agent,” which can complete purchase steps (selection, payment, confirmation) with limited or no human clicks.3
Historically, “B2A2C” has also been used to describe machine-mediated service interactions (e.g., contact center automation) and machine-mediated business processes reaching consumers.2
How it relates to marketing
B2A2C shifts parts of marketing and commerce from human-facing experiences (ads, landing pages, checkout flows) toward agent-facing interfaces (structured product data, policies, APIs, and machine-readable offers). In practice, this can change:
- Discovery: AI agents may substitute or reduce traditional search, browsing, and comparison behaviors. 2
- Conversion paths: AI agents can handle multi-step journeys (research → compare → purchase) and may transact using standardized protocols and payment integrations. 2
- Channel measurement: referral and conversion attribution increasingly includes “AI/agent” sources (e.g., generative AI interfaces driving retail traffic). 2
How to calculate (the term)
Business to Machine to Consumer (B2M2C) is a model, not a single metric, so organizations typically track B2M2C penetration and performance using a small set of operational measures.
B2M2C Revenue Share (%)
Formula: B2M2C Revenue Share = (Revenue from AI/agent-mediated orders ÷ Total revenue) × 100
Agent Conversion Rate (%)
Used for agent-originated sessions, requests, or intents.
Formula: Agent Conversion Rate = (Agent-mediated purchases ÷ Agent-mediated purchase intents or sessions) × 100
Offer Acceptance Rate (%)
Used when your system returns quotes or options to buyer agents.
Formula: Offer Acceptance Rate = (Accepted offers ÷ Offers returned to agents) × 100
Agent Coverage (%)
Measures how much of the catalog is purchasable through an agent interface (API, protocol, or structured flow).
Formula: Agent Coverage = (SKUs/services purchasable via agent interface ÷ Total SKUs/services) × 100
Where available, teams also track protocol-level funnel steps (for example, product retrieval, eligibility checks, payment authorization, and confirmation), especially when using agentic commerce standards.
How to utilize (the term)
Common B2A2C use cases in an agentic-commerce environment include:
- Agent-friendly product discovery: exposing enriched, structured product data so buyer agents can compare options.1
- Programmatic purchase flows: enabling agents to initiate checkout, confirm fulfillment details, and complete payment via standardized integrations (for example, agentic commerce protocols built with payment providers). 2
- Replenishment and subscriptions: repeat purchases where the agent acts based on preferences, constraints, and inventory/availability. 1
- Customer service and post-purchase automation: returns, order status, modifications—often the earliest “B2A2C” implementations before full agentic checkout. 1
Compare to similar approaches
| Model | Primary intermediary | Who “decides” | Who “executes” the transaction | Typical marketing emphasis |
| B2C | None (direct to consumer) | Human consumer | Human consumer | Creative, UX, persuasion, segmentation |
| B2B2C | Business partner platform/reseller | Human consumer (often) | Platform + consumer | Partner enablement, channel ops |
| D2C | Brand-owned channel | Human consumer | Human consumer | Owned channels, first-party data |
| B2A2C (agentic) | AI agents (buyer + seller agents) | Consumer intent, interpreted by buyer agent | Buyer agent (often) via protocols/APIs | Structured data, machine-readable offers/policies, agent distribution |
| A2A commerce (subset of B2A2C) | Two interacting agents | Buyer agent using consumer preferences/constraints | Buyer agent | Agent interoperability, policy clarity, integration readiness |
Agentic commerce and agent-to-agent commerce are widely discussed as the mechanism enabling B2A2C-style purchasing at scale.
Best practices
- Make product and policy data machine-readable: consistent identifiers (SKU/GTIN where relevant), pricing rules, availability, shipping/returns, warranties, and eligibility constraints. 1
- Instrument “agent funnels” separately: treat AI/agent sources as a distinct channel in analytics and attribution to avoid mixing them into generic referral buckets. 1
- Support standardized agentic checkout integrations where applicable: align with emerging protocols and payment-provider patterns intended for agent-mediated purchasing. 2
- Define permissioning and consent boundaries: explicitly constrain what a buyer agent can do (spend limits, categories, return permissions), and log actions for auditability.
- Design for dispute/exception handling: agent-friendly paths for cancellations, substitutions, returns, and customer support escalation. 1
Future trends
- Growth of agent-to-agent marketplaces and “agent distribution”: brands competing for visibility in environments where agents—not humans—choose which merchants to query first.
- Emerging paid placement and monetization models for agent ecosystems: parallels to search ads, but optimized for agent evaluation/ranking.
- Standardization of agent commerce protocols and interoperability: open specifications intended to make agent purchasing portable across platforms and payment processors.
- More measurable “AI referral” and conversion impact: continued growth in traffic originating from generative AI interfaces, changing channel mix and reporting requirements.
Related Terms
- Agentic commerce
- Agent-to-agent (A2A) commerce
- Buyer agent
- Seller agent
- Agentic Commerce Protocol (ACP)
- Model Context Protocol (MCP)
- Product information management (PIM)
- Structured product data (schema/feeds)
- API-first commerce
- Conversational commerce
- Digital commerce analytics (AI referral attribution)
- Business to Consumer (B2C)
- Business to Business to Consumer (B2B2C)
References
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