Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents
Fuente:
arXiv
Salvato in:
| Autore principale: | |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866916547444867072 |
|---|---|
| author | Bousetouane, Fouad |
| author_facet | Bousetouane, Fouad |
| contents | The evolution of agentic systems represents a significant milestone in artificial intelligence and modern software systems, driven by the demand for vertical intelligence tailored to diverse industries. These systems enhance business outcomes through adaptability, learning, and interaction with dynamic environments. At the forefront of this revolution are Large Language Model (LLM) agents, which serve as the cognitive backbone of these intelligent systems. In response to the need for consistency and scalability, this work attempts to define a level of standardization for Vertical AI agent design patterns by identifying core building blocks and proposing a \textbf{Cognitive Skills } Module, which incorporates domain-specific, purpose-built inference capabilities. Building on these foundational concepts, this paper offers a comprehensive introduction to agentic systems, detailing their core components, operational patterns, and implementation strategies. It further explores practical use cases and examples across various industries, highlighting the transformative potential of LLM agents in driving industry-specific applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_00881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents Bousetouane, Fouad Multiagent Systems The evolution of agentic systems represents a significant milestone in artificial intelligence and modern software systems, driven by the demand for vertical intelligence tailored to diverse industries. These systems enhance business outcomes through adaptability, learning, and interaction with dynamic environments. At the forefront of this revolution are Large Language Model (LLM) agents, which serve as the cognitive backbone of these intelligent systems. In response to the need for consistency and scalability, this work attempts to define a level of standardization for Vertical AI agent design patterns by identifying core building blocks and proposing a \textbf{Cognitive Skills } Module, which incorporates domain-specific, purpose-built inference capabilities. Building on these foundational concepts, this paper offers a comprehensive introduction to agentic systems, detailing their core components, operational patterns, and implementation strategies. It further explores practical use cases and examples across various industries, highlighting the transformative potential of LLM agents in driving industry-specific applications. |
| title | Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2501.00881 |