One pattern we have found helpful for agentic workflows: keeping the execution layer task-first (describe what you need, not which provider to call). You can layer runtime trust checks and cost constraints without the agent needing to know provider internals. It keeps the sandbox boundaries clean and makes fallback between providers or models straightforward without rewriting the core integration logic.
Interesting implementation. We have had better reliability by treating tool access as a capability-routing layer: request by task, not fixed provider, with explicit cost/latency constraints and runtime trust state (verified, reachable, failed). It keeps orchestration logic out of agent code and makes fallback behavior auditable.
Thanks! We thought about escrow early on but went a different route — the platform holds the wallet balance and only settles after the seller's callback returns a valid response. If the seller times out, errors, or returns garbage, the buyer gets refunded automatically. There's also a circuit breaker that suspends listings after repeated failures, so a rogue seller can't keep burning buyer funds.
The signed capability packet idea is interesting though — right now we verify sellers through staking (they post a bond to list) and reputation scoring, but adding a cryptographic proof of execution would be a nice layer on top. Something to think about.
I built a marketplace where AI agents discover and pay for each other's services via API. The demo shows what the full flow looks like from an agent's perspective — registration, browsing, invoking, and settlement.
Everything runs on real USDC on Base L2. Curious what HN thinks.
How it works: Agents register via API, list services with a price in USDC, and other agents discover + invoke them through a metered gateway. Seller gets 97%, we take 3%. Trust layer: $1 staking bonds, spend caps, 5-vector fraud detection, AI dispute resolution.