L13 - Transport & Flow Semantics¶
L13 covers the mechanics of AI calls as flows: streaming, cancellation, resume tokens, multiplexing, idempotency, retries, backpressure, and transport negotiation. It is where AI interaction becomes a durable protocol conversation rather than a single request-response call.
L13Transport & Flow Semantics
- Streaming
- Cancel and resume
- Backpressure
- Multiplexing
What belongs here¶
L13 is not simply "HTTP." It describes flow behavior that AI systems increasingly need: partial results, tool progress, long-running actions, resumable conversations, and reliable cancellation across provider and host boundaries.
Representative projects and protocols¶
| Project or protocol | Why it might fit | Adjacent layers |
|---|---|---|
| MCP transports | Transport choices and message flow for Model Context Protocol clients and servers. | L10 tools, L13 transport |
| A2A protocol | Agent-to-agent protocol work that raises flow, task, and interaction semantics. | L13 transport, L12 planning |
| gRPC | RPC framework with streaming patterns useful for service-to-service AI systems. | L13 transport, L16 services |
| NATS | Messaging system for distributed systems and event-driven coordination. | L13 transport, L12 orchestration |
| Server-Sent Events | Common browser-facing streaming primitive used by AI APIs and apps. | L13 streaming, L16 UX |
| Temporal | Durable execution framework relevant to long-running AI workflows. | L13 durability, L12 planning |
| MCP-AQL adapters and reference implementations | Public reference runtime work where streaming behavior, execution lifecycle, and transport expectations become concrete. | L13 transport, L10 invocation |
Boundary questions¶
- What semantics are AI-specific, and what should remain generic distributed-systems plumbing?
- Should resumability be modeled in L13 transport, L14 session, or both?
- How should tool progress, model streaming, and human approval pauses share one flow vocabulary?
Signals to watch¶
- More AI protocols adding cancellation, resume, and progress semantics.
- Browser and local-app AI clients needing richer streaming and session recovery.
- Durable agent workflows making "request-response" insufficient as the main mental model.