AI Agents vs. AI Copilots: What They Are and When to Deploy Them
Guardado en:
| Publicado en: | Machine Design (Aug 18, 2025) |
|---|---|
| Autor principal: | |
| Publicado: |
Endeavor Business Media
|
| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3240918448 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 0024-9114 | ||
| 022 | |a 1944-9577 | ||
| 035 | |a 3240918448 | ||
| 045 | 0 | |b d20250818 | |
| 084 | |a 28336 |2 nlm | ||
| 100 | 1 | |a Kuntz, Chris | |
| 245 | 1 | |a AI Agents vs. AI Copilots: What They Are and When to Deploy Them | |
| 260 | |b Endeavor Business Media |c Aug 18, 2025 | ||
| 513 | |a News | ||
| 520 | 3 | |a When determining which approach best suits a manufacturing operation, several factors should be considered, including process complexity and variability; risk tolerance and safety; workforce skills and readiness; and integration requirements. Think of them as intelligent digital assistants providing guidance, support, insights and recommendations, while leaving final decisions and actions to human operators. In manufacturing contexts, AI agents might autonomously monitor equipment health and trigger maintenance workflows, proactively identify training needs for frontline workers, analyze production data to automatically adjust process parameters, independently manage inventory replenishment based on usage patterns, identify quality issues and initiate corrective actions or orchestrate complex workflows across multiple systems. | |
| 653 | |a Parameter identification | ||
| 653 | |a Workers | ||
| 653 | |a Agents (artificial intelligence) | ||
| 653 | |a Technology adoption | ||
| 653 | |a Digital transformation | ||
| 653 | |a Trouble shooting | ||
| 653 | |a Decision making | ||
| 653 | |a Workforce | ||
| 653 | |a Manufacturers | ||
| 653 | |a Complexity | ||
| 653 | |a Automation | ||
| 653 | |a Manufacturing | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Efficiency | ||
| 653 | |a Process parameters | ||
| 653 | |a Skills | ||
| 653 | |a Labor productivity | ||
| 653 | |a Large language models | ||
| 653 | |a Computer aided engineering--CAE | ||
| 653 | |a Artificial intelligence | ||
| 773 | 0 | |t Machine Design |g (Aug 18, 2025) | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3240918448/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3240918448/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |