From Token Output to Real-Time Signals
Token-by-token output is natural language. It is hard to treat as a system signal, so in practice you wait for the full response and then interpret it.
As planning and instruction-following became core expectations, structured output emerged as a key capability: it turns text into parseable data. Agently further stabilizes structured output at an engineering level, even with smaller models, so output control becomes a system guarantee.
The remaining bottleneck is timeliness: structured output is typically parseable only after completion. Instant streaming solves this by parsing structured output during generation, so partial structured information becomes real-time signals.
Why partial signals matter
When partial structure is available early, you can branch, trigger tasks, and update state before the full result is done. The stream stops being “just text” and becomes a signal flow.
How this connects to TriggerFlow
TriggerFlow is event-driven. Once partial structured information becomes a signal, it can be emit()‑ted and when()‑ed. That makes model streaming a first-class input to an event orchestration system.
Business impact in practice
Embodied AI
In robotics or device scenarios, a single request may produce reasoning, action commands, and user-facing text/voice. Real-time structured signals let actuation, feedback, and safety handling run in parallel, reducing latency to the user and the environment.
Agent systems
Plans often need to be decomposed into concurrent tasks. Partial structured signals can trigger tool calls, retrieval, and sub‑tasks early, instead of waiting for the full output, enabling more flexible orchestration and higher parallelism.
Engineering implications
- Earlier reactions: respond to key fields without waiting for full completion.
- Stronger orchestration: flows depend on signals, not whole texts.
- More reliable delivery: structured output stability + Instant parsing enables complex signal systems.