Agentic AI
AI systems that autonomously plan, use tools, and iterate toward goals with memory and reasoning
Agentic AI refers to systems that operate with agency—the ability to set goals, make decisions, and take actions autonomously toward achieving those goals, rather than simply responding to queries. Unlike a chatbot that processes a single input and returns an output, agentic systems maintain state through memory, decompose complex tasks into subtasks, invoke external tools (APIs, databases, search engines), and adapt their approach based on intermediate results. They function as actors within environments, capable of multi-step planning, error recovery, and self-correction.
The architecture of agentic AI typically includes a planning layer (where goals are broken into actionable steps), a tool-use layer (where the system invokes external capabilities), and a feedback loop (where results inform subsequent actions). Many agentic frameworks—from ReAct (Reasoning + Acting) to more recent designs—combine large language models with retrieval systems, calculators, and APIs. The model serves as the "brain," reasoning about what action to take next, while tools extend its capabilities beyond text generation. Memory systems allow agents to learn from past interactions and maintain context over long task sequences.
Agentic AI matters because it moves AI beyond narrow, single-turn utility into systems that can handle real-world complexity. Rather than humans breaking down problems for the AI, agentic systems can break down problems themselves. This unlocks applications in research, customer service automation, code generation and debugging, and autonomous decision-making. However, agentic systems also introduce new challenges: hallucination and tool misuse become more consequential, reasoning traces become harder to audit, and ensuring alignment with human intent across multi-step executions is more difficult than with supervised, deterministic systems.
