AI Streaming Cuts Out at 90%: What 'Continue' Actually Does Under the Hood
As AI output shifts from human reading to machine consumption — JSON for APIs, HTML for rendering, tool calls for real-world actions — a mid-stream break stops being a minor annoyance and becomes a data integrity and safety problem. Handling it correctly means separating raw deltas, parse state, and commit state, and never executing a half-formed tool call.
A dropped AI stream isn't just a missing sentence. For structured outputs like JSON, HTML, or tool-calling parameters, a mid-stream break produces unparseable objects, broken page layouts, or duplicate side effects like double charges. The fix depends entirely on where the break occurred — client connection, upstream model request, or the generation service itself. Only client-side disconnects allow precise replay; anything deeper forces a semantic re-continuation that can shift style, repeat content, or corrupt structure. Production systems decouple the generation task from the client subscription, persist every output chunk as a sequenced event log, and treat half-finished payloads as display-only drafts until validation passes.
Most products conflate three distinct failures — client drop, model request failure, and service crash — under a single 'continue' button, which hides very different recovery guarantees from the user.
The principle that 'half-finished output can be displayed but never parsed or executed' is a useful boundary that applies across plain text, code, JSON, and agent tool calls.
Decoupling the generation task from the client connection is an architectural choice that turns a brittle streaming experience into a resilient background job, but it requires deliberate state management that many early AI integrations skip.