Agentic AI: Autonomous Intelligence Beyond Generative AI
In recent years, generative AI systems like ChatGPT, Gemini, Copilot, Grok, DALL·E, Midjourney to name a few have captivated the world by producing human-like text and creative imagery on demand. These large language models (LLM) models and their derivatives operate as powerful content generators, responding to user input prompts with essays, code, or art. Now, a new frontier of AI is emerging – one that moves from passive content generation to active decision-making and autonomous action. We are talking about agentic AI, where AI ‘agents’ have ‘agency’. can pursue goals, make choices, and act with minimal human intervention.
Generative AI vs. Agentic AI
Generative AI refers to AI systems designed to ‘generate’ or ‘create original content‘ — text, images, videos, audio, or code — typically in response to a user’s query prompts. Generative systems which are powered by advanced deep learning models, learn patterns from vast datasets through machine learning and use that knowledge to produce new, human-like outputs when prompted. For example, given a request, a generative model can draft an essay, a research report or a realistic image. Models like ChatGPT and DALL·E are prime examples.
Agentic AI describes systems endowed with ‘agency’ i.e. the ability to autonomously make decisions and act towards intended goals with limited or no continuous human guidance. An agentic AI system goes beyond producing content. It can take action in an environment (theoretically, digital and/or physical), plan multi-step tasks, adapt to changes, and continuously learn from feedback as it works towards a predefined objective. In essence, agentic AI combines the generative capabilities with decision-making frameworks and tools that let it execute tasks on a user’s behalf.
Agentic AI proactive rather than reactive. It does not wait for a prompt at each step, but can autonomously initiate and coordinate actions to reach a goal. This typically involves a loop of perceiving the environment or context, reasoning about what to do, acting to change the state or retrieve information, and learning from the results.
In practical terms, an agentic AI might use a large language model (LLM) like GPT-4 or GEMINI as a “brain” for reasoning, but augment it with the ability to move forward to call software tools, query databases, control robots, or interact with web services. Rather than just outputting an answer, it might, for example, autonomously plan a travel itinerary: searching flights, comparing prices, booking tickets, and emailing you the confirmation, all based on a high-level goal like “plan me a 5-day trip to London within this budget.” The key distinction is autonomy: generative AI generates results when asked, whereas agentic AI can act on its own to achieve a goal once given a general directive.
