Why every lab pivoted toward 'agents' at roughly the same time
For the first couple of years after ChatGPT's breakout, the dominant framing of AI products was conversational: you type a question, the model answers, you read it, the turn ends. Through 2025 and into 2026, that framing has been steadily displaced across the entire industry by 'agentic' products — systems that can take a goal, break it into steps, use tools (a browser, a code interpreter, a file system, other software) across multiple turns without a human in the loop at every step, and keep working until the goal is met or it needs help. Coding agents that can read a codebase, write and run code, and fix their own errors are the clearest example, and arguably the place agentic behavior has proven most genuinely useful so far.
This wasn't a coincidence of marketing — it reflects a real technical maturation. Models got meaningfully better at tool use, longer-context reasoning, and self-correction, which made multi-step autonomous action reliable enough to be worth shipping. It's also a natural response to a ceiling on the old framing: a chatbot that answers one question well is valuable, but an assistant that can actually complete a task end to end is worth paying for in a different, larger way. Every major lab now ships some version of this — coding agents, browser-using agents, 'computer use' agents that operate a desktop GUI — because the economic upside of task-completion is so much larger than the upside of just answering questions.