The distinction between a "model" and an "agent" is dissolving. Google's new Interactions API provides a single interface for both, signaling a future where flagship releases are complete systems out-of-the-box, capable of both simple queries and complex, long-running tasks, blurring the lines for developers and users.

One vision pushes for long-running, autonomous AI agents that complete complex goals with minimal human input. The counter-argument, emphasized by teams like Cognition, is that real-world value comes from fast, interactive back-and-forth between humans and AI, as tasks are often underspecified.

Treat advanced AI systems not as software with binary outcomes, but as a new employee with a unique persona. They can offer diverse, non-obvious insights and a different "chain of thought," sometimes finding issues even human experts miss and providing complementary perspectives.

In this software paradigm, user actions (like button clicks) trigger prompts to a core AI agent rather than executing pre-written code. The application's behavior is emergent and flexible, defined by the agent's capabilities, not rigid, hard-coded rules.

While language models are becoming incrementally better at conversation, the next significant leap in AI is defined by multimodal understanding and the ability to perform tasks, such as navigating websites. This shift from conversational prowess to agentic action marks the new frontier for a true "step change" in AI capabilities.

AI is best understood not as a single tool, but as a flexible underlying interface. It can manifest as a chat box for some, but its real potential is in creating tailored workflows that feel native to different roles, like designers or developers, without forcing everyone into a single interaction model.

The 'agents vs. applications' debate is a false dichotomy. Future applications will be sophisticated, orchestrated systems that embed agentic capabilities. They will feature multiple LLMs, deterministic logic, and robust permission models, representing an evolution of software, not a replacement of it.

The next frontier in AI is not just developing individual agents, but orchestrating teams of them. Users will move from dialoguing with a single chatbot to managing multiple agents working in parallel on complex, long-running workflows. This becomes a new core skill for knowledge workers.

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.