{"data":{"slug":"agents","etype":"concept","name":"AI Agents","cat":"Training & Inference","subtitle":"Models that act: plan, call tools, iterate — the 2025 product wave.","summary":"An agent loops a model through plan → act → observe: calling tools, browsing, writing files, correcting itself until the goal is met. Coding agents (Claude Code, Codex, Devin) proved it commercially; standards like MCP wire agents to the world. Agents multiply token consumption per task by orders of magnitude — the demand driver behind the inference buildout — and concentrate the safety questions, since acting models can do more than talking ones.","attrs":null,"kf":null,"aliases":["agentic AI","autonomous agents"],"ticker":null,"exchange":null,"hq":null,"founded":null,"website":null,"status":null,"region":null,"asof":"2026-01-15","sources":[{"url":"https://modelcontextprotocol.io/","label":"MCP"}]},"meta":{"found":true,"count":1,"served_from":"site cache (no live upstream calls)","as_of":"2026-10-14T02:56:51+00:00","disclaimer":"Research and information only — not investment advice. Structured records carry basis labels (vendor spec / reported / estimate); market quotes are delayed/indicative.","attribution":"Records compiled from vendor documentation, official filings and reputable public reporting; market quotes via financial data providers. See /data-sources."}}