The Industry Is Shifting to 'Agentic AI'

The core architectural shift from conversational to agentic AI involves moving beyond prompt-and-response to systems that can perceive their environment, set goals, make decisions, and execute multi-step tasks with minimal human oversight. These systems are designed for continuous learning and adaptation, using components for perception, reasoning, and execution to operate autonomously. At MWC Barcelona 2026, this trend was prominent, with the theme "The IQ Era" reflecting the industry's move toward embedded, proactive AI. Samsung unveiled its Galaxy S26 as an "agentic AI phone," featuring proactive AI that anticipates user needs, suggests actions based on context, and can manage multi-step tasks in the background across different apps. For instance, its AI can read a group chat about a pizza order, open a food delivery app, build the cart, and await final confirmation from the user. Beyond consumer devices, agentic AI is being deployed for large-scale infrastructure management. Render Networks launched ClearWay at MWC, an agentic AI architecture for automating and governing deployments like fiber broadband and electric grids. Its specialized agents can validate work in real-time and autonomously approve tasks, aiming to reduce manual errors and accelerate project timelines. Google Cloud and Nokia also announced a collaboration to integrate agentic AI with network APIs, enabling developers to create applications for industries like automotive and logistics. In San Francisco, startups are actively building and deploying agentic AI for internal operations. Companies like Atomicwork, Leena AI, and Rezolve AI are developing autonomous agents to handle IT support, employee service desks, and DevOps tasks, automating complex workflows within platforms like Microsoft Teams. San Francisco-based Apollo.io recently launched an AI Assistant that functions as an agentic go-to-market operating system, automating tasks from prospecting to campaign execution. For engineers exploring this space, the rise of agentic AI creates a distinction from traditional Machine Learning Engineer roles. While ML engineers focus on building and training models, an AI Engineer building agentic systems concentrates on system design, integrating models with external tools via APIs, and orchestrating complex workflows. This requires strong software engineering fundamentals, proficiency in Python, and experience with frameworks like LangChain, AutoGen, or CrewAI. Engineers can start building with several open-source agentic frameworks. LangGraph, with 34.5 million monthly downloads, is popular for enterprise applications requiring state management. Microsoft's AutoGen is a multi-agent conversation framework, while CrewAI focuses on orchestrating role-playing AI agents for collaborative tasks. Google also offers the Agent Development Kit (ADK) for integration with its ecosystem.