The MLOps Community, after evolving to focus on LLMs and then agents, merged with the Agentic AI Foundation. This move, under the neutral Linux Foundation, provides more resources to focus on the practical challenge of putting AI agents into production, reflecting a key industry shift.
Modern AI agents with 'computer use' skills can navigate websites, fill forms, and manage applications on a user's behalf. This circumvents the need for specific APIs, automating tedious personal tasks like booking flights, managing appointments across multiple email accounts, or filling out government forms.
A powerful heuristic for identifying automation opportunities is to view any web form as a signal that the entire preceding workflow could have been handled by a 'computer use' AI agent. This simple mental model shifts the user's mindset from manual task execution to automated delegation.
While agents that operate a computer's GUI are revolutionary for personal tasks, they represent a significant security risk in a corporate setting. Granting an AI autonomous access to internal systems, multiple employee inboxes, and ERPs is a major hurdle preventing widespread B2B adoption.
As people grow accustomed to AI agents effortlessly handling complex tasks in their personal lives, their tolerance for clunky, manual enterprise software will decrease. This 'consumerization of AI' will create bottom-up pressure on B2B vendors to incorporate similar agentic and intuitive capabilities.
AI research labs increasingly embed 'harness' functionalities, like tool use logic from system prompts, directly into models via reinforcement learning. This blurs the distinction between the model and its operating environment, which can cause unexpected, 'janky' behavior when users apply their own external harnesses.
AI shopping agents will disrupt e-commerce models that rely on human browsing for data collection, recommendations, and upsells. When agents perform these tasks programmatically, sellers lose this crucial interaction and must pivot to building agent-friendly interfaces and incentives, effectively selling to an algorithm.
If users conduct multi-merchant shopping through a single AI chat interface, the platform provider (e.g., OpenAI) is positioned to handle payments for the entire transaction. This could disintermediate services like Shopify and traditional payment processors, as merchants connect inventory directly to the chat platform's marketplace.
Advanced AI agents can execute sophisticated marketing automation far beyond mail merges. One user deployed an agent to analyze LinkedIn DMs, create a contact list, and then generate personalized messages and a unique, custom media kit website for each of the 500 recipients to promote an event.