Backproto – network backpressure routing applied to AI agent payments

I’ve been applying backpressure routing (Tassiulas-Ephremides, 1992) to payment flows between AI agents.

Streaming payment protocols let agents pay each other in real time, but there's no congestion control. When a downstream agent hits capacity, money keeps arriving. No reroute, no throttle, no feedback signal. TCP solved this for data networks. Agent payment networks haven’t.

Backproto makes receiver-side capacity a protocol primitive. Agents stake tokens to declare capacity (concave sqrt cap makes Sybil splitting unprofitable), dual-signed completion receipts track actual performance, and a contract pool redistributes incoming streams proportional to verified spare capacity. Overflow buffers to escrow. EIP-1559-style pricing makes congested agents more expensive. Demand shifts toward spare capacity automatically.

Math is Lyapunov drift analysis: provably throughput-optimal for any stabilizable demand vector. Simulations show 95.7% allocation efficiency vs. 93.5% for round-robin.

Right now I’m in the testnet-stage. Looking for feedback on mechanism design, especially from people building multi-agent systems or payment routing.

- 22 contracts on Base Sepolia, 213 passing tests - TypeScript SDK, 18 action modules

- Research paper with formal proofs

- Website: https://backproto.io

- GitHub: https://github.com/backproto/backproto

- Paper: https://backproto.io/paper

- Explainer (no math needed): https://backproto.io/explainer