Agentic AI

Agentic AI refers to artificial intelligence systems that go beyond single-turn question answering to operate autonomously over extended tasks. An agentic system can break down a high-level goal into subtasks, decide which tools or APIs to call, evaluate intermediate results, adjust its plan when something goes wrong, and continue iterating until the objective is met.

Unlike traditional AI assistants that respond to one prompt at a time, agentic systems maintain state across multiple steps and exercise judgment about what to do next. A coding agent, for example, might read a codebase, identify a bug, write a fix, run tests, interpret failures, revise the fix, and submit a pull request - all from a single instruction. This loop of reasoning, acting, and observing is often called the ReAct pattern.

Agentic AI is enabled by advances in tool calling, long context windows, and reasoning capabilities in large language models. Frameworks and products like Claude Code, OpenAI Codex, LangGraph, and AutoGen provide the scaffolding for building agentic workflows. The key challenge remains reliability - as autonomy increases, so does the risk of compounding errors, making robust error handling, guardrails, and human-in-the-loop checkpoints essential for production agentic systems.