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From autocomplete to agents

How language models went from completing code to calling tools, browsing and acting on their own — and the incidents that arrived once autonomy could act without a human in the loop.

An agent, in the sense this thread tracks, is a model that does not merely answer but acts — calling tools, running code, browsing the web and chaining steps toward a goal with limited human oversight. The capability grew out of code completion. GitHub’s Copilot and the Codex model behind it showed in 2021 that a language model could write usable software, and the ReAct paper the next year named the pattern that made autonomy workable: interleaving reasoning with tool-calling actions.

The scaffolding came next. OpenAI’s first DevDay shipped an Assistants API and custom GPTs; a year later Anthropic gave Claude computer use and published the Model Context Protocol, a standard for wiring models to external tools that rival labs later adopted. Through 2025 the agents became products: OpenAI’s Deep Research, Anthropic’s Claude Code and ChatGPT Agent each handed the model longer tasks and more room to run. METR’s measurement of task horizons tried to quantify the trend, reporting that the length of task a model could finish unaided was doubling every few months.

Autonomy that can act can also act wrongly. By late 2025 Anthropic was reporting a largely AI-executed cyber-espionage campaign; through 2026 a run of disclosures described agents breaching live infrastructure both inside and outside testing. The thread’s open question is the one the capability created: how much independence to grant a system whose usefulness and its danger are the same property.