TLDR:
Agentic AI is the category of AI systems that take sequences of actions, use tools, and make decisions autonomously to complete a goal—rather than simply responding to single prompts. This is the dominant AI architecture pattern driving enterprise adoption in 2025-2026.
How Agentic Systems Differ from Traditional AI
Traditional AI applications respond to a single input with a single output. Agentic AI systems decompose goals into subtasks, plan execution paths, call external tools (APIs, databases, code execution, web browsers), observe results, and iterate until success or failure. The “agent property” emerges from the infrastructure giving the model tools, memory, and a loop of action and observation. Modern frameworks like LangChain, OpenAI Agents SDK, and Anthropic’s Claude Code embody this pattern.
Categories of Agentic AI
Major categories include coding agents (Claude Code, Cursor, Devin, Replit Agent), research agents (deep research products from OpenAI, Anthropic, Perplexity), customer support agents (Intercom Fin, Sierra), browser/computer-use agents (Anthropic computer use, Google Mariner), and increasingly specialized business agents for sales prospecting, marketing operations, finance reconciliation, and HR onboarding. Multi-agent systems use multiple specialized agents collaborating through protocols like Google’s A2A.
Risks and Governance
Agentic AI introduces risks beyond traditional AI: unbounded action loops consuming resources or causing harm, tool misuse, prompt injection through external content the agent reads, hallucination amplification across multi-step workflows, and difficulty auditing autonomous decisions. Production agents require careful permission scoping (least-privilege tool access), observability infrastructure, human-in-the-loop checkpoints for high-stakes actions, and rigorous evaluation. The EU AI Act and emerging US AI governance frameworks impose additional obligations on agentic systems making consequential decisions.