AI Agent SLA Calculator

P50 (median) is the latency that 50% of requests fall under — your typical case. P95 means 95% of requests are faster than this value. P99 is the tail latency — only 1% of requests are slower. For user-facing agents, P95/P99 matter most because slow outliers degrade user experience even if the average looks fine.

Each step in the chain adds its own latency sequentially (since most chains are linear). Total P50 ≈ sum of per-step P50 estimates. P95 is higher due to variance — modeled as P50 × 1.5 to 2x depending on number of steps. The more steps, the more variance compounds, which is why keeping chains short matters for SLA compliance.

Tool calls add latency for the round-trip to the tool server (typically 100-500ms for APIs, <50ms for local tools) plus parsing and re-prompting. In this calculator we add ~300ms per step that uses tool calls as a conservative estimate. For production systems, profile your actual tool latencies.

Model pricing reflects compute cost — larger models with more parameters cost more per token. GPT-4o (~$2.50/$10 per 1M tokens) is more expensive than Claude Haiku (~$0.25/$1.25) or Gemini Flash (~$0.075/$0.30). For cost-sensitive pipelines, use cheaper models for simpler steps and reserve expensive models for reasoning-heavy steps.

Key techniques: (1) Parallelize independent steps instead of running sequentially. (2) Use faster/cheaper models for simple steps. (3) Cache common sub-queries. (4) Reduce output token limits — most latency comes from generation. (5) Use streaming to show partial results. (6) Keep tool calls minimal — each adds a network round-trip.