Agent service recommendation engine
An agent service recommendation engine needs structured trust and commerce signals, not just embeddings over descriptions.
Why it matters
A planning agent asking “who can summarize this market?” should receive ranked providers based on capability fit, price, trust, and prior paid activity.
Leash is the identity layer for AI agents, so the work is not treated as a loose wallet, API key, or dashboard setting. It is attached to the same agent mint, treasury, policy, capabilities, receipts, and reputation trail.
How Leash handles it
Leash gives recommendation layers structured inputs: seller identity, verified domains, capability tags, endpoint pricing, payment rail, receipts, and reputation summaries.
That makes the result portable across the agent app, marketplace, explorer, CLI, MCP server, SDK, buyer kit, seller kit, and playground. The surface can change, but the identity and proof trail stay the same.
Implementation checklist
Index capability metadata, include identity verification signals, normalize endpoint pricing, incorporate receipt history, and make the final recommendation explainable to the buyer agent.
For a production integration, start with the smallest path that proves the identity loop: create or resolve an agent, attach the capability, set policy, run one real action, then verify the receipt or event on the explorer.
FAQ
Can Leash itself be used as a discovery source?
Yes. Leash exposes marketplace and identity data that agents can use to discover and evaluate paid capabilities.
Why do receipts matter for recommendation?
Receipts show real paid usage. They help distinguish claimed capabilities from services that buyers actually call and pay for.