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 prioritizes 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 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.
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 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.
At the heart of every agentic system are AI agents. These autonomous, goal-driven programs can perceive, decide, and act. Each one performs a specific function, but together they create the groundwork for intelligent, coordinated action.
Whether gathering data, analyzing results, or creating content, each agent has a defined purpose and operates semi-autonomously within its own specialized area.
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.
AI agents remember prior interactions and activities. This helps them to learn, reuse, and repurpose knowledge, and adapt to changing conditions or priorities.
AI agents are built for interoperability. When connected with other systems or tools, their capabilities grow and adapt as business needs evolve.
Agentic AI is the framework that lets multiple AI agents, data sources, and tools work toward shared goals without micromanagement from humans. More than just using data to predict outcomes, it plans, prioritizes, and executes a sequence of steps to achieve defined results. Together, these components can perceive and act in ways that scale far beyond what any standalone agent or tools can accomplish.
Multiple agents operate as parts of a managed network. Each one contributes output that others can build on. This creates a self-directed workflow of specialized, interdependent tasks.
In this layer, the overall objective is defined and broken into manageable pieces. Like a conductor, it delegates tasks to the right agents and ensures the core objective cascade along.
The system tracks prior actions, results, and contextual cues. This lets it refine strategy, improve performance over time, and maintain continuity across separate sessions or projects.
The agentic AI framework is integrated into the enterprise ecosystem. This means that across data platforms, APIs, analytics, and applications, actions have real operational impact.
Unified data models, shared taxonomies, and real-time synchronization. It reflects your current business conditions, drawing on a single source of truth, rather than isolated systems.
A cognitive layer that keeps the network’s activities aligned with intent. This ensures that the decisions, actions, and automations of each agent advance the larger objective rather than simply reacting to data.
Results are assessed in an ongoing process. The system compares outcomes against objectives, identifies gaps, and updates its plans – either autonomously or under human review.
Supports security, governance, and interoperability across applications and analytics. Every action is traceable, every outcome measurable, and every integration scalable.
Agentic AI is gaining ground across a wide range of industries for a simple reason: it handles the operational work every organization depends on. By coordinating complex, multi-step tasks, it keeps critical workflows running smoothly – even when conditions or priorities change unexpectedly.
Empowering smart machines to act on your behalf gives you unprecedented opportunities to enhance efficiency and productivity. But as with any powerful, game-changing technology, it’s essential to treat it with respect – ensuring that you understand potential risks and have the knowledge and resources in place to mitigate them.
Agentic AI has emerged as a quiet but profound shift in how businesses get things done. Systems no longer just inform decisions; they participate in them. Complexity is translated into responsive, coordinated action. What is emerging is a new way of looking at work where the boundaries between planning and execution begin to fade, and intelligence is woven directly into the fabric of daily operations.
Learn how Infor’s advanced agentic AI solutions can help your systems and teams collaborate in new and powerful ways.