it_support · workflow
incident.io reduces chatbot latency by 50% with speculative tool calling
incident.io's incident-management chatbot suffered multi-second delays because LLM tool calls require sequential round-trips — the model decides to call a tool, the tool runs, the result returns, and only then does the model continue — producing nearly 5 seconds of wait time for a simple 'pause incident' action.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · User sends incident request
A user types a natural language command to the chatbot, such as asking it to pause an incident.
Tools used
LLMGo
Outcome
Speculative tool calling saves 2–3 seconds per interaction and reduces latency by about 50% for most users, making the chatbot feel responsive rather than sluggish.
What failed first
Prompt optimization alone was insufficient to meet latency targets; all available LLM-level and database-level performance improvements had already been applied before the speculative approach was developed.
Results
Time saved2-3s
Volumeabout 50%
Grounding & classification
Source type: technical build writeup
21 fields verified against source quotes.
agentic workflowchatbotconversational aichat transcriptfailure mode describedmetric backednamed customerproduction runtime claimedtools describedworkflow describedsoftwarecycle time reductionresponse time reductiontechnical build writeupit supportagentic task execution