Source URL: https://www.docker.com/blog/genai-vs-agentic-ai/
Source: Docker
Title: GenAI vs. Agentic AI: What Developers Need to Know
Feedly Summary: Generative AI (GenAI) and the models behind it have already reshaped how developers write code and build applications. But a new class of artificial intelligence is emerging: agentic AI. Unlike GenAI, which focuses on content generation, agentic systems can plan, reason, and take actions across multiple steps, enabling a new approach to building intelligent, goal-driven…
AI Summary and Description: Yes
**Summary:** The text discusses the emerging field of agentic AI, comparing it with generative AI (GenAI). It highlights the unique capabilities of agentic AI, such as planning and executing multi-step tasks, and emphasizes the integration of Docker tools in the development of both GenAI and agentic AI applications. This information is valuable for developers and security professionals looking to understand the evolving landscape of AI technologies.
**Detailed Description:**
– **Generative AI (GenAI)**:
– Defined as AI systems that generate content (text, code, images) based on prompts.
– Operates predominantly as a prediction engine, where models predict the next output (e.g., next word, token, or pixel).
– Examples include ChatGPT, Claude, GitHub Copilot.
– Top use cases are found in coding, content creation, education, and automated chat interactions.
– **Agentic AI**:
– Described as AI systems designed to plan, reason, and act independently to achieve specific goals.
– Unlike GenAI, it does not solely rely on static prompts but can engage in multi-step reasoning.
– Examples include AI agents like OpenAI’s ChatGPT agent and Cursor’s agent mode.
– Notable use cases encompass customer service, fraud detection, and IT operations.
– **Comparison between GenAI and Agentic AI**:
– **Adoption**: GenAI is widely adopted; agentic AI is in early-stage deployment (only 14% of companies have fully implemented).
– **Development Process**: GenAI relies on prompt selection and integration, whereas agentic AI requires defining workflows, model selection, and orchestration.
– **Challenges**: GenAI’s challenges include model selection and output consistency, whereas agentic AI presents complexities in task orchestration and security implications.
– **Integration with Docker**:
– The text emphasizes Docker’s role in supporting the development of both GenAI and agentic AI applications through tools like the Docker Model Runner and MCP Toolkit.
– Provides flexibility in running models locally, maintaining privacy and data control, while also leveraging cloud capabilities when needed.
– **Applications and Examples**:
– The document outlines several starter projects and examples from building chatbots to autonomous multi-agent systems, demonstrating practical uses of both GenAI and agentic AI.
– **Security Considerations**:
– As agentic AI systems resemble microservices, they introduce expanded security surfaces due to their accessibility to sensitive tools and data. This highlights the need for secure architecture in development practices.
– **Conclusion**:
– The narrative concludes by urging developers to embrace AI proficiency, highlighting the potential of both GenAI and agentic AI, while recognizing the challenges that come with increased complexity and security needs.
This analysis is crucial for professionals in AI and security, underscoring the importance of understanding the distinctions and capabilities of emerging AI technologies, particularly in regards to their security implications and integrated development environments.