Mastering Agentic AI: Agents vs. Workflows Explained
- Authors
- Published on
- Published on
In this thrilling episode of Real Terms with AI Season 2 by Google Cloud Tech, we dive headfirst into the fascinating world of AI, leaving no byte unturned. Last season, we explored the very building blocks of generative AI, data protection, and the intricate dance of function calling. But hold onto your hats, this season is all about agentic concepts, with a twist of different video formats to keep you on the edge of your seat. The team sets the stage by unraveling the enigma of agents and agentic behaviors, shedding light on the crucial role of non-determinism in tasks.
As the discussion unfolds, we learn that agentic behaviors are the secret sauce that blends tasks seamlessly to achieve specific outcomes, a stark departure from the more predictable nature of deterministic workflows. AI agents take center stage, showcasing their ability to autonomously make decisions to reach predefined goals, while workflows play it safe with a more calculated approach. From processing invoices to creating websites, examples abound to illustrate the power of agentic workflows and AI agents in action.
The team delves deeper into the essence of agentic behavior, drawing parallels to human interactions and decision-making processes. The million-dollar question arises: when should one opt for an agentic workflow over creating a full-fledged AI agent? The answer lies in the complexity of the task at hand and the level of autonomy desired. By weighing the risks and rewards, one can navigate the fine line between harnessing the power of AI agents and sticking to tried-and-true workflows. With a promise to share practical code snippets and architectural examples on GitHub, viewers are invited to embark on a thrilling journey of exploration into the realm of agents and agentic workflows.
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Image copyright Youtube
Watch Agentic AI: Workflows vs. agents on Youtube
Viewer Reactions for Agentic AI: Workflows vs. agents
Request for AI topics to be discussed in the season
Questions about switching between different AI workflows
Suggestion for edits on how Gemini handles code alterations
Feedback on the video's definitions and clarity
Comment on potential improvements for Gemini's editing techniques
Inquiry about where function calling fits in
Request for the GitHub repo mentioned in the video
Comment on the hosts being a cute couple
Mention of making room for videos and photos
One comment finding the content boring
Related Articles
Mastering Real-World Cloud Run Services with FastAPI and Muslim
Discover how Google developer expert Muslim builds real-world Cloud Run services using FastAPI, uvicorn, and cloud build. Learn about processing football statistics, deployment methods, and the power of FastAPI for seamless API building on Cloud Run. Elevate your cloud computing game today!
The Agent Factory: Advanced AI Frameworks and Domain-Specific Agents
Explore advanced AI frameworks like Lang Graph and Crew AI on Google Cloud Tech's "The Agent Factory" podcast. Learn about domain-specific agents, coding assistants, and the latest updates in AI development. ADK v1 release brings enhanced features for Java developers.
Simplify AI Integration: Building Tech Support App with Large Language Model
Google Cloud Tech simplifies AI integration by treating it as an API. They demonstrate building a tech support app using a large language model in AI Studio, showcasing code deployment with Google Cloud and Firebase hosting. The app functions like a traditional web app, highlighting the ease of leveraging AI to enhance user experiences.
Nvidia's Small Language Models and AI Tools: Optimizing On-Device Applications
Explore Nvidia's small language models and AI tools for on-device applications. Learn about quantization, Nemo Guardrails, and TensorRT for optimized AI development. Exciting advancements await in the world of AI with Nvidia's latest hardware and open-source frameworks.