The Missing Runtime for Long-Running AI Agents

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Enterprise AI agents need more than stronger models. They need durable execution environments that can coordinate multi-step workflows, survive failures, pause for human review and resume reliably after disconnects or delays. AI Agents Have Moved Beyond Chat Demos AI agents work beautifully in demos. A user asks a question, a large language model generates a response and everyone sees possibility. Production systems are different. Consider what these systems are now asked to do. An agent assessing a change request may pull deployment history, evaluate blast radius, check freeze-window policy, wait for a release manager’s sign-off and then schedule the rollout. An agent adjudicating an insurance claim may extract fields from submitted documents, cross-check them against prior claims, apply underwriting rules and route anything unusual to a human adjuster. An agent triaging a security alert may enrich indicators, correlate against past incidents, assess asset criticality and hold containment until an analyst approves it. Those three workflows have almost nothing in common at the domain level. They share a shape: Multiple steps, several systems, independent failure modes and at least one point where a person decides. They take minutes or hours, not milliseconds. The challenge is no longer only intelligence; it is execution. Many enterprise AI conversations focus on model selection, prompt engineering, retrieval-augmented generation and tool calling. Those are important, but they do not answer what happens after the agent begins executing long-running work across multiple …

Vollständiger Originalartikel ↗

• Betroffenheit im eigenen Stack prüfen: Versionen/Komponenten abgleichen.
• Originalquelle / Hersteller-Advisory lesen: https://devops.com/the-missing-runtime-for-long-running-ai-agents/
• Verfügbaren Patch oder Workaround einspielen und dokumentieren.