Agentic AI Explained So Anyone Can Get It!
AI Summary
This video explains what agentic AI is, highlighting how it is different from autonomous AI by emphasizing its proactive capabilities. Agentic AI perceives, reasons, acts, and learns autonomously with minimal human input, enabling it to execute complex tasks like coding, ticket solving, and web searching. The core mechanism involves a loop of perception (data gathering), reasoning (planning and decision-making with LLMs like GPT-4), action (executing tasks via APIs, code, commands), and learning (continuous improvement based on results).
The video includes real-world examples such as AI agents deploying code updates autonomously, demonstrating how these agents go beyond reactive assistants by working proactively. It further breaks down how to build such agents using four main components: a language model as the brain, memory layers to retain context over time, tools/APIs to perform actions, and an orchestration framework to coordinate the workflow.
Additionally, it covers the Model Context Protocol (MCP) as a critical framework for managing interaction among multiple agents and tools, providing structure and context to complex multi-step AI workflows. The presenter encourages software engineers to start exploring agentic AI and notes that this shift represents a fundamental change in automation and AI application beyond conversational systems.