How Do You Reduce AI Agent Latency?

AI agent latency accumulates across model inference, tool calls, context retrieval, and state operations, with repeated loops and network round trips making production performance less predictable than prototype testing. Reducing delays requires measuring each operation separately, colocating inference, execution, tools, and data where possible, and applying techniques such as caching, parallelization, streaming, smaller intermediate models, and resilient timeout and retry policies. Azion proposes a distributed architecture in which AI Inference provides model access, Functions runs agent logic and potentially MCP servers, KV Store manages distributed session state, and SQL Database combines persistent memory with vector search, reducing dependence on separate services. Functions supports five-minute wall-clock executions but limits active CPU time to two seconds and fetch calls to 50 per invocation, so agent loops may need batching, caching, or chained invocations. The approach concerns optimizing a single agent’s infrastructure rather than coordinating multiple agents, and emphasizes that understanding the number and duration of operations is often more important than optimizing any individual model or prompt.

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