AI Summary

Enterprise AI agents are entering a rebuild era as organizations confront reliability challenges in production. Teams find that LLM performance alone does not guarantee success; long‑running workflows must survive crashes, preserve state, recover from failures, manage inference costs, and coordinate across APIs, tools, and enterprise systems. After an initial wave of rapid deployment, companies are revisiting first‑generation implementations. They are redesigning early agent architectures to emphasize workflow orchestration, observability, governance, and recovery. Preeti Somal, Senior VP Engineering at Temporal Technologies, highlighted these needs at the AI Impact Series event in New York.

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