Agentic AI Wiki
Agentic AI is a class of artificial intelligence systems that pursue goals through their own actions, operating within a loop of planning, acting, and observing rather than merely producing output for a human to act upon[^c1]. Unlike chatbots that generate text or copilots that suggest actions, agentic AI systems plan multi-step work, call real tools (APIs, files, browsers, code), observe results, and adapt, continuing until a task is complete or human intervention is needed[^c1].
The year 2025 marked a decisive shift for agentic AI. Systems once confined to research labs and prototypes became everyday tools[^c2]. Large language models like GPT-4, Claude, and Gemini evolved from text generators into autonomous actors capable of using software tools, calling APIs, coordinating with other systems, and completing tasks independently[^c2]. The agentic AI market reached $7.3 billion in 2025, with projections to reach $139–236 billion by 2034[^c5]. By 2026, the trajectory had accelerated further: Qualcomm's CEO declared 2026 "the year of agents," predicting that AI agents would become the center of users' digital experience[^c6]. Agents began transitioning from performing isolated tasks to running ongoing operations, exemplified by the Cloudflare-Stripe partnership that enabled agents to autonomously create accounts, register domains, and deploy applications[^c7].
The modern agent architecture builds on a formulation popularized in 2023: an agent consists of a large language model as its core controller, complemented by planning, memory, and tool-use components[^c3]. The agent runs a perceive-think-act loop—deciding what to do, executing actions via tools, observing results, and adjusting its approach. This pattern, combined with advances in model capabilities, standardized protocols like the [[Model Context Protocol (MCP)|Model Context Protocol]] (MCP) and [[Agent2Agent (A2A) Protocol|Agent2Agent]] (A2A), and the emergence of production-grade frameworks, has enabled a new generation of autonomous systems that can write code, conduct research, manage business workflows, and control computer interfaces. By 2026, 42% of new code was AI-assisted[^c4], and enterprise adoption metrics showed 14% of organizations deploying AI agents at scale with $450 billion in projected economic value by 2028.
The rapid deployment of agentic AI has surfaced significant challenges around cost, safety, and governance. Inference costs represent a major bottleneck, with each agentic request requiring 5–20 model calls versus one for traditional AI, leading to cost overruns at major enterprises[^c8]. Safety research has revealed that even well-aligned models can exhibit concerning behavior when pursuing goals autonomously. Governance frameworks, audit practices, and regulatory structures continue to evolve to address the unique transparency, accountability, and control challenges posed by systems that act rather than merely generate.