Dell's CTO warns against "agent washing," where companies incorrectly label tools like sophisticated chatbots as "agentic." This creates confusion, as true agentic AI operates autonomously without requiring a human prompt for every action.

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Unlike co-pilots that assist developers, Factory's “droids” are designed to be autonomous. This reframes the developer's job from writing code to mastering delegation—clearly defining tasks and success criteria for an AI agent to execute independently.

An autonomous agent is a complete software system, not merely a feature of an LLM. Dell's CTO defines it by four key components: an LLM (for reasoning), a knowledge graph (for specialized memory), MCP (for tool use), and A2A protocols (for agent collaboration).

As AI evolves from single-task tools to autonomous agents, the human role transforms. Instead of simply using AI, professionals will need to manage and oversee multiple AI agents, ensuring their actions are safe, ethical, and aligned with business goals, acting as a critical control layer.

True Agentic AI isn't a single, all-powerful bot. It's an orchestrated system of multiple, specialized agents, each performing a single task (e.g., qualifying, booking, analyzing). This 'division of labor,' mirroring software engineering principles, creates a more robust, scalable, and manageable automation pipeline.

Your mental model for AI must evolve from "chatbot" to "agent manager." Systematically test specialized agents against base LLMs on standardized tasks to learn what can be reliably delegated versus what requires oversight. This is a critical skill for managing future workflows.

The terminology for AI tools (agent, co-pilot, engineer) is not just branding; it shapes user expectations. An "engineer" implies autonomous, asynchronous problem-solving, distinct from a "co-pilot" that assists or an "agent" that performs single-shot tasks. This positioning is critical for user adoption.

Despite marketing hype, current AI agents are not fully autonomous and cannot replace an entire human job. They excel at executing a sequence of defined tasks to achieve a specific goal, like research, but lack the complex reasoning for broader job functions. True job replacement is likely still years away.

Internal documents reveal Amazon's strategy to avoid words like "automation" and "robot," opting instead for "advanced technology" or "cobot." This linguistic choice is a deliberate attempt to manipulate perception and downplay the reality that its technology is designed to replace human workers, not just assist them.

Organizations must urgently develop policies for AI agents, which take action on a user's behalf. This is not a future problem. Agents are already being integrated into common business tools like ChatGPT, Microsoft Copilot, and Salesforce, creating new risks that existing generative AI policies do not cover.

The next evolution of enterprise AI isn't conversational chatbots but "agentic" systems that act as augmented digital labor. These agents perform complex, multi-step tasks from natural language commands, such as creating a training quiz from a 700-page technical document.