What is agentic AI?
A new era of intelligence is emerging in which AI doesn’t just analyze data but actually uses it to inform purposeful action. Agentic AI bridges insight and execution, infusing your systems with the power to operate with intent, adaptability, and accountability.
Agentic AI marks a turning point in the digital age where enterprise AI turns from passive intelligence to proactive collaborator. It prioritises and then acts – integrating across your systems to coordinate a framework of tools and individual AI agents. It understands objectives, sequences, and steps, calling on the right resources to get things done. The result is a continuous loop of action, feedback, and improvement – helping teams conquer complexity by better orchestrating the systems they already use every day. This leads to not only smarter automation, but greater clarity and the ability to adapt quickly – while keeping a laser focus on the goal.
Agentic AI definition
Agentic AI is an artificial intelligence system that demonstrates agency – the ability to act autonomously towards achieving a specific goal rather than following a set of predefined instructions. It consists of multiple AI agents – machine learning models that mimic human decision making – that perform the sub-tasks necessary to reach the goal in a coordinated way.
Agentic AI vs. AI agents
The difference between AI agents and agentic AI is subtle but significant. It relates to how much independence and coordination each brings to the table. AI agents are software entities that act as focused problem-solvers. They sense, plan, and take action within a predefined goal or task. Agentic AI takes things a step further. It manages and aligns many agents, tools, or workflows to accomplish more complex results. If you look at AI agents as musicians in an orchestra, each skillfully performing their part, then agentic AI is the conductor. It sets the pace, direction, and harmony so that everyone plays what and when they should, and the symphony comes together beautifully.
Generative AI vs. agentic AI
Generative AI has been one of the hottest – if not the hottest – technology topic since the arrival of ChatGPT in 2022. GenAI uses machine learning and large language models (LLMs) to generate outputs based on prompts or inputs. As we all know, its ability to generate text, images, videos, and code is growing more powerful every day. While GenAI focuses on creating content, agentic AI takes it a step further by applying generative outputs towards specific goals – and adding reasoning, planning, and action without waiting for a prompt. Agentic AI can, for example, not only tell you the best place to view the next solar eclipse, it can also book your travel and accommodation. Learn more about generative vs. agentic AI.
AI agents: The building blocks of agentic AI
At the heart of every agentic system are AI agents. These autonomous, goal-driven programmes can perceive, decide, and act. Each one performs a specific function, but together they create the groundwork for intelligent, coordinated action.
Decision makers
Whether gathering data, analysing results, or creating content, each agent has a defined purpose and operates semi-autonomously within its own specialised area.
Goal oriented
Agents don’t run on rigid scripts. They have explicit objectives that they work toward – evaluating and adjusting approaches as they move toward that goal.
Context aware
AI agents remember prior interactions and activities. This helps them to learn, reuse, and repurpose knowledge, and adapt to changing conditions or priorities.
Scalable
AI agents are built for interoperability. When connected with other systems or tools, their capabilities grow and adapt as business needs evolve.