Agentic Commerce

The Thesis

Commerce is shifting from search-and-browse to agent-mediated. A user tells their agent what they want. The agent discovers options, evaluates them against the user’s declared preferences, and executes a purchase — all with the user’s trust. That trust requires three things traditional commerce infrastructure doesn’t provide:

Transparent product intelligence — Agents need structured, scored product attributes with explicit provenance. They need to know why a product is recommended, not just that it’s recommended.
User-owned intent profiles — Preferences, constraints, and avoidances that the user controls and the agent consults — never sold to advertisers.
Machine-native payments — Agents need to pay for things programmatically, without card forms or browser sessions. USDC over HTTP 402 gives agents a payment rail that works like an API call.

Podium provides all three.

Architecture

The Two-Lane Provenance Model

Every signal in Podium carries an explicit SignalSource:

Source	Origin	Weight in Feed
`USER_DECLARED`	Campaign votes, quiz answers, explicit preferences	70%
`MARKET_DERIVED`	Enrichment pipeline — product reviews, structured APIs, community data	30%
`BASELINE_INFERRED`	Computed aggregates from enrichment signals	Used in attribute baselines

This is a deliberate architectural choice. Enrichment data is not synthetic intent — it’s structured market intelligence that makes the platform useful during cold-start while first-party user data grows organically. As users interact (vote in campaigns, rank products, purchase), USER_DECLARED signals naturally dominate the intent score. Market-derived data gracefully steps back to a supplementary validation role. Agents consuming the product feed receive provenance metadata on every item:

{
  "intentScore": 0.42,
  "intentProvenance": {
    "USER_DECLARED": { "voteCount": 12, "weight": 0.7 },
    "MARKET_DERIVED": { "signalCount": 87, "weight": 0.3 }
  }
}

The agent — and the user — always know what’s driving a recommendation.

Key Components

Enrichment Pipeline

Multi-source product intelligence: ingest, extract, resolve, normalize, baseline. The data engine behind informed agent decisions.

Agentic Product Feed

The endpoint agents call to discover products — scored, provenanced, and filterable. No auth required.

x402 Payments

Machine-native USDC payments over HTTP 402. Agents pay for products (or API access) with a single fetch call.

Beauty Companion

A complete reference implementation: a personal shopping agent built on these primitives with Telegram, AI recommendations, and Privy wallets.

Companion API

The Companion API (/companion/*) provides generalized infrastructure for building personal agents:

Endpoint	Purpose
`GET/POST/PATCH /companion/profile/{userId}`	Intent profile CRUD — preferences, constraints, avoidances
`GET /companion/products`	Filterable product catalog with category, brand, price range, and search
`POST /companion/interactions`	Record typed interactions: `RANK_UP`, `RANK_DOWN`, `SKIP`, `PURCHASED`, `PURCHASE_INTENT`, `NUDGE_OPENED`
`GET /companion/recommendations/{userId}`	AI-ranked product recommendations based on profile + interaction history
`POST /companion/orders`	Create a concierge order (the agent handles fulfillment)
`GET /companion/user/by-telegram/{telegramId}`	Link external identities to Podium users

The Companion API is vertical-agnostic. The Beauty Companion uses it for skincare; you could build the same pattern for fashion, food, supplements, or any product domain. The schema fields (skin type, concerns, etc.) are profile-specific — the infrastructure (interactions, recommendations, orders) is generic.

For Developers

If you’re building an agent on Podium, the typical integration path is:

Create user and intent profile

Use the Companion API to create a user and build their preference profile through conversational onboarding, quiz mechanics, or direct input.

Discover products

Query the agentic product feed for scored, provenanced product listings. Filter by category, price range, or attribute baselines.

Record interactions

As the user expresses preferences (likes, dislikes, skips), record interactions. These feed back into recommendation ranking and intent scoring.

Execute commerce

Create checkout sessions and pay via x402 (USDC) or Stripe. The concierge order model handles fulfillment on the user’s behalf.

Earn trust over time

The more the user interacts, the better the recommendations. First-party signals naturally overtake market-derived data in the intent score.

Overview

Intelligence Layer

Commerce Primitives

Building on Podium

Agentic Commerce

The Thesis

Architecture

The Two-Lane Provenance Model

Key Components

Enrichment Pipeline

Agentic Product Feed

x402 Payments

Beauty Companion

Companion API

For Developers

​The Thesis

​Architecture

​The Two-Lane Provenance Model

​Key Components

Enrichment Pipeline

Agentic Product Feed

x402 Payments

Beauty Companion

​Companion API

​For Developers

The Thesis

Architecture

The Two-Lane Provenance Model

Key Components

Companion API

For Developers