Reliability Controls for LLM and Agent Workflows
Explore how to implement reliability controls for large language models and agent workflows by applying distributed system patterns, managing retries, idempotency, circuit breakers, and graceful degradation. Learn to design systems that maintain bounded latency, control costs, and preserve side-effect safety while handling partial failures effectively.
Dashboards and alerts tell a human when to intervene. They do not make individual tool calls or dependency failures safe by themselves. Failure handling must be built into the workflow itself. Classic distributed-systems reliability patterns still apply to LLM and agent workflows, but the failure modes differ in ways that affect architecture decisions. A retry can produce a different output because the model is non-deterministic, and tool results or retrieved context can also change between attempts. An agent loop can turn a single user request into multiple model calls with increasing context length and cost. The reliability objective is not identical output across repeated runs. It is bounded time and spend per request, controlled, idempotent side effects for tool operations, and defined degraded behavior when a dependency slows or fails.
Specify reliability as a set of controls across the full request path: retrieval, one or more tool calls, a ...