LLM applications do not fail like classic application programming interfaces. A web API under load usually degrades in predictable ways: latency rises, error rates spike, and dashboards show a clear capacity boundary. Agentic systems are different. They fail silently, returning confident answers built on partial context, truncated tool results, or timeouts that the agent masks with a plausible narrative. In governed analytics, reliability is a policy requirement, not just a performance metric.
Many teams start with static requests-per-second limits because they are simple and familiar. But tool-calling workloads are bursty, multi-step, and coupled to expensive downstream systems such as data warehouses, vector stores, and metadata catalogs. A single user question can fan out into dozens of tool calls — schema lookups, semantic layer resolution, SQL compilation, query execution, lineage checks, and policy validation. Under real usage, static limits either block legitimate work or allow a noisy-neighbor agent to starve everyone else, especially when agents retry aggressively or enter loops.