Why Your AI Agent Dies in Production (And What to Do About It) You deploy an AI research agent. It works perfectly in demos. It searches the web, calls APIs, writes files, loops through 50 documents. Then on day three, the server restarts mid-task. The agent starts over from scratch—wasting API tokens, duplicating writes, and confusing downstream systems. This is not a rare edge case. Long-running AI agents fail constantly in production because most frameworks treat each LLM call as a fire-and-forget operation with no memory of what already happened. The agent's state lives in memory, and when memory disappears, so does the progress. Temporal solves this. Its durable execution model records every step of your workflow as an immutable event history. If the process dies at step 47 of 100, Temporal replays the event log and resumes at step 48—not step 1. In February 2026, Temporal raised a $300M Series D at a $5B valuation, led by Andreessen Horowitz.…