inngest-agents

Durable AI agent workflows survive server crashes by orchestrating model calls and tool executions through cached, retriable steps. This approach ensures long-running agentic processes maintain state across deploys without losing progress or duplicating side effects.

Human-in-the-loop approval steps use durable waits to pause automated AI agent workflows for external signals. This mechanism holds execution without blocking server resources until a human approves the next action, ensuring safe progression through complex tasks.

Managing state and side effects in long-running AI agents requires a durable execution environment with provider-level flow control. Caching model calls and wrapping tool executions in retriable steps prevents duplicate side effects during unexpected failures or server restarts.

Realtime progress updates stream directly to the UI from within the durable workflow. This allows users to monitor agent status and multi-step tool-calling loops live as the background process executes.

Auditing a current agent implementation for crash-safety involves wrapping existing model calls in durable steps. This transitions fragile executions into a crash-safe environment where tool-calling loops are cached and automatically retried upon failure.

AI agent workflows fail or duplicate actions during server deployments because standard executions lack durable state. Wrapping model calls and side effects in durable steps caches their results, ensuring the workflow resumes correctly without re-running completed actions.