The Missing Middle: Why Signals Aren't Enough for AI Streaming
Chronicle 26 - Arc 7: From Library to Platform
Signals made local reactivity pleasant again. AI products added a different class of problem: long-lived streams with cancellation, retries, checkpoints, and cross-boundary flow control.
That gap is the missing middle.
Where signals shine
Signals are excellent for:
- synchronous local state
- deterministic dependency tracking
- UI-focused update propagation
If your work is mostly in-memory and immediate, signals are hard to beat.
Where AI streaming pushes beyond signals
Agent and LLM workflows need more:
- stream composition across network/system boundaries
- explicit cancellation and reset propagation
- orchestration of retries, timeouts, and partial completion
- inspectable status across multi-step pipelines
At that point, "just values changing" is not enough context.
Why callbag-native orchestration helps
Callbag-style sources and control signals let you model both data and lifecycle in one graph:
- data chunks flow as normal emissions
- control flows through explicit signal channels
- adapters bridge Promise and async iterable boundaries immediately
You can reason about ongoing work, not just current values.
Balanced view
This is not "signals are bad." It is "signals are incomplete for this class of problem."
Many teams will still use signals at UI edges. The missing middle is the platform layer that coordinates streaming, control, and state coherently behind that UI.
Takeaway
AI streaming architecture needs a runtime that understands flow, not only snapshots.
Signals solve a critical piece. Reactive orchestration fills the rest.