The Shift from Chatbots to Long-Horizon Agents
In 2023 a chatbot answered a question in one turn. In 2026 a frontier model routinely runs minutes to hours on a single task. METR's Time Horizon 1.1 benchmark (January 2026) puts Claude Opus 4.6 at 14+ hours of expert work at 50% reliability. The horizon has been doubling roughly every seven months since GPT-2. Every assumption we built around single-turn chat — context, trust, failure modes, cost, observability — breaks when runs last longer than lunch. A chatbot is a stateless function. It takes a prompt, returns a reply, and forgets. Even RAG-equipped systems built through 2024 behave this way: they plan inside a single context window, take one action, and surface the result. An autonomous agent is different in kind. It runs a loop. It decides when to stop. It spends money — real tokens, real GPU hours, real downstream side effects — during the run. Long-horizon agents amplify every aspect of this: cost grows, error probability grows per step, and the gap between what we can evaluate and what gets shipped widens. The numbers from METR make this concrete. Between GPT-2 and Claude Opus 4.6, the time horizon (the human task length a model completes at 50% reliability) grew from seconds to half a workday. The doubling time sits near seven months. If the trend holds another…
The Shift from Chatbots to Long-Horizon Agents: In 2023 a chatbot answered a question in one turn. In 2026 a frontier model routinely runs minutes to hours on…
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