At its core, agentic AI is about creating systems that can “get things done” with a degree of independence. The real and tangible aspects of agentic AI today include the ability to automate complex, multi-step tasks that were previously beyond the reach of simple automation. For example, an agentic system could be tasked with “planning a business trip to New York,” and it would then proceed to book flights, reserve a hotel, and add appointments to a calendar, all without step-by-step human intervention.
Modern agentic AI can also interact with other software and APIs, allowing it to pull information from a database, send an email, apply a patch, or interact with a website to complete its tasks. Additionally, agentic capabilities are being integrated with large language models (LLMs). This means that instead of just generating text, an LLM can now use its generated plan to take actions in the digital world.
In industry-specific settings, we are seeing real-world applications of agentic AI. In customer service, for example, AI agents can handle complex queries that require accessing and updating customer records. In finance, they can perform market analysis and execute trades based on predefined parameters. In cybersecurity, they can identify and respond to threats in real-time.