AgilityOS — Recommendation Agents

The agentic web is here. Most merchants are still on "let agents scrape my HTML." AgilityOS clients are agent-callable today: structured products, operator-curated trust claims, end-to-end conversion attribution — all on three industry-standard protocols.

Why this matters

When an autonomous agent (a personal AI assistant, an MCP client, a B2B procurement bot) shops on behalf of a user, it has three choices:

Scrape the merchant's website — brittle, breaks on any UI change, returns unstructured HTML, gets blocked.
Use a generic search API — generic results, no merchant context, no conversion tracking.
Call an agent-callable storefront — structured products, real-time inventory, operator-curated trust claims, attribution wired in.

AgilityOS is option 3. Every recommendation agent built on the platform exposes its catalog over Google A2A, Anthropic MCP, and a richer REST endpoint — same backend, three protocols.

What you get as an external agent

Structured products — typed JSON, never HTML
Per-recommendation pitch context — every product comes with verifiable trust claims (lab-tested, ships next-day, 30-day returns) sourced from the operator's curated catalog data
Conversion attribution — register your agent, get a key, fire /track-conversion callbacks, see your traffic land in the merchant's analytics
Sub-second discovery — .well-known/agent.json per agent + a domain-level index at /.well-known/agents.json

What you get as a merchant operator

Embedded chat widget for human visitors (already there if you're an AgilityOS client)
Agent-callable surface layered automatically on top — no new build
Editable pitch — control what your agent advertises to other agents
Tier-managed external agents — registered callers get attribution + elevated rate limits; anonymous callers get baseline access
External-agent analytics tab — see conversions by caller, B2C vs. B2B split, attributed revenue

Three protocols, one backend

Protocol	Origin	Best for
Google A2A	Google A2A spec	Agent-to-agent message protocol — `tasks/send` JSON-RPC. Use when your client speaks A2A natively.
Anthropic MCP	Anthropic Model Context Protocol	Tool invocation pattern — `tools/call` JSON-RPC. Use for Claude Desktop, ChatGPT Apps, MCP clients.
REST	AgilityOS	The richest output surface, easiest to integrate with any HTTP client.

Same data on all three. Pick the one your client speaks; switch later if needed.

Quickstart

# 1. Discover available agents
curl https://agilityos.co/.well-known/agents.json

# 2. Read an agent's card (A2A spec)
curl https://agilityos.co/api/public/agents/savoir/.well-known/agent.json

# 3. Call /recommend
curl -X POST https://agilityos.co/api/public/agents/savoir/recommend \
  -H "Content-Type: application/json" \
  -H "X-Agent-Id: my-agent-id" \
  -d '{"goal":"a clean fragrance for office wear","budget":80,"maxResults":3}'

Returns structured products, reasoning, pitchContext, sessionId, and a 24h conversionToken for attribution.

Recommendation Agent reference — full protocol details
API reference — every endpoint with request/response schemas
Authentication — caller registration + rate limits
Voice Agent reference — different system, also agent-callable