What is Agentic AI?

In simple terms, Agentic AI is the shift from building AI models that are passive tools to creating AI systems that are active doers.

Think of the difference between a spellchecker and an editor:

• A Non-Agentic AI (The Spellchecker): It is a passive tool. It identifies a potential error and waits for you, the user, to command it to fix it. It has no goal beyond reacting to the text you write.
• An Agentic AI (The Autonomous Editor): You give it a high-level goal: “Edit this document for clarity, tone, and grammar.” It then proactively reads, analyzes, rewrites sentences, and reorganizes paragraphs on its own to achieve that goal. It makes its own sub-tasks and executes them.

“Agentic AI” is the design philosophy focused on creating that autonomous editor, not just the better spellchecker.

Core Characteristics of Agentic AI

An AI system is considered “agentic” when it demonstrates these key traits:

1. Proactivity and Initiative: It doesn’t just respond to a direct command. It takes initiative to achieve a goal. It anticipates needs, performs unstated intermediate steps, and continues working until the objective is met.
2. Sophisticated Planning & Reasoning: This is the heart of agentic behavior. The AI can take a complex, high-level goal (e.g., “Organize my team’s offsite event”) and decompose it into a logical sequence of tasks (e.g., 1. Poll team for dates. 2. Research venues. 3. Get quotes. 4. Book venue. 5. Send calendar invites.).
3. Dynamic Tool Use: This is a critical enabler of modern agentic AI. The system understands that it has limitations and can autonomously choose, use, and combine external “tools” to accomplish its tasks. These tools can be:
   • Web browsers for searching and scraping information.
   • Code interpreters for running calculations or data analysis.
   • APIs for booking flights or sending messages.
   • Other AI models for generating images or analyzing data.
4. Memory and Self-Reflection: An agentic system can remember past actions, learn from its mistakes, and reflect on its performance to improve its strategy. If a chosen plan fails, it can analyze why and attempt a different approach—much like a human would.