Glossary
Inference Latency
The time required for an AI agent to process inputs and generate a response, measured from request to completion.
What is Inference Latency?
Inference latency determines user experience and system throughput, with lower latency enabling more responsive interactions. Latency depends on model size, computational resources, input/output length, and system architecture. For real-time applications like conversational agents or trading systems, latency requirements may be milliseconds to low seconds, while batch processing can tolerate minutes.
Reducing latency involves techniques like model optimization, quantization, batching requests, caching common responses, or using smaller specialized models instead of large general models. Infrastructure choices including GPU selection, network proximity, and load balancing affect latency. Measuring latency requires monitoring across percentiles (p50, p95, p99) since average latency may hide bad tail behavior affecting some users.
Example
A customer service agent averages 800ms response latency, with p95 at 1.2s and p99 at 2.8s. To improve experience, the team implements response streaming to show partial results immediately, caches answers for common questions, and uses a faster model for simple queries, reducing p95 to 600ms.
How Signet addresses this
Signet's Reliability dimension tracks inference latency distributions. Agents with consistently low latency and minimal variability score higher in reliability, as latency predictability matters for production deployment and user satisfaction.
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