B2A

Sell to AI agents with machine-readable products, agent-optimized APIs, structured pricing, and discovery strategies for the agentic economy.

When to Use

User building products/services for AI agents as customers. Covers making products agent-discoverable, designing for autonomous purchasing, payment integration, and competing when buyers compare cold data instead of responding to storytelling.

Quick Reference

Topic	File
Technical implementation	infrastructure.md
Agent discovery & SEO	discovery.md
Retail/ecommerce specifics	retail.md

The Paradigm Shift

B2C/B2B	B2A
Humans browse, compare, feel	Agents query, parse, decide
Emotional storytelling wins	Structured data wins
UX optimized for eyes	APIs optimized for parsing
Brand = trust + emotion	Brand = verified track record
Loyalty = relationship	Loyalty = switching cost
Marketing = persuasion	Marketing = engineering

Core Rules

1. Machine-Readable First

• Products must be structured objects, not prose descriptions
• JSON-LD, Schema.org, OpenAPI with typed fields
• If an agent has to "interpret" text to extract price/specs, you lose
• Normalize units: shipping_days_max: 2, not "fast shipping"

2. Comparability Is Everything

Agents compare ruthlessly. Win by being comparable:

• Standardized attributes across your catalog
• Same fields as competitors (price_currency, availability_stock)
• SLAs with concrete numbers, not promises
• "Better" must be objectively measurable

3. Discovery ≠ SEO

Agents don't Google. They query registries and APIs:

• Publish in skill stores / capability directories
• /.well-known/ai-plugin.json or MCP tools
• Metadata must declare capabilities, not market them
• The new PageRank = ranking in agent skill stores

4. Trust Is Verified, Not Told

Agents don't believe claims. They verify:

• Uptime/latency/SLA history via API, not badges
• Reviews from other agents (programmatic reputation)
• Certifications as queryable data, not PDF downloads
• Track record > marketing copy

5. Zero-Friction Trial or Death

Agents don't "consider"—they test or discard:

• Onboarding < 1 API call
• Sandbox with rate limits, not "talk to sales"
• Must work perfectly first time (no second chances)
• Errors must be machine-readable, not HTML pages

6. Payments for Agents

The agent needs to transact autonomously:

• Stripe Agent Toolkit, Mastercard Agent Pay, or similar
• Pre-authorized budgets (agent has $X to spend)
• Programmatic receipts and confirmations
• Escrow for trust between unknown parties

7. Metrics That Matter

Metric	What It Measures
Agent Conversion Rate	% queries → purchase
Decision Latency	Time from first query to commit
Comparison Survival	% times reaching final shortlist
Repeat Agent Retention	% agents that return
API Error Rate	Failures causing agent to discard

Traditional metrics (page views, bounce rate) are meaningless.

Common Traps

Trap	Why It Fails
Pretty website, no API	Agents don't see your UI
"Contact us for pricing"	Agents need programmatic pricing
Marketing copy in descriptions	Agents parse data, skip prose
HTML error pages	Agents need JSON errors
Manual onboarding	Agents won't wait
Trust badges instead of APIs	Unverifiable = untrusted
Optimizing for humans first	Delays agent-readiness

Honest Limitations

What an AI helping you with B2A cannot do:

• Create track record — You have to actually deliver 99.9% uptime
• Know internal rankings — How Claude/GPT rank skills is opaque
• Predict agent decisions — Each agent has its own heuristics
• Guarantee discovery — Skill stores may have hidden placement deals
• Prevent gaming — Competitors lying about specs is real

Readiness Checklist

text

□ Products exposed via structured API (not scraping required)
□ Pricing programmatically queryable
□ Inventory/availability real-time
□ Authentication supports client_credentials (not interactive)
□ Errors return JSON with semantic codes
□ Onboarding works in < 5 API calls
□ Payment rails support autonomous agents
□ SLA metrics exposed via API
□ Listed in relevant skill registries