Agentic AI

For several years, most business use of generative AI followed a simple pattern: a person asks, the model answers. Agentic AI describes the move beyond that pattern, toward AI systems that pursue goals rather than just respond to prompts. An agentic system can decide which steps to take, call tools and systems to gather information or make changes, check whether its actions worked and adjust its approach. It may work alone on a task or coordinate with other agents and with people. The result is AI that can carry out work, not just assist with it.

The term became prominent in 2024 and 2025 as model capabilities improved and companies began building agents into products and business processes. Researchers and practitioners, including Andrew Ng, described agentic design patterns such as reflection, tool use, planning and multi-agent collaboration that make AI systems more capable. Agentic AI is often used as an umbrella term covering individual AI agents, agentic workflows and multi-agent systems. It brings new opportunities for automation and new risks, since systems that act can also make mistakes with real consequences. For executives, technology and AI leaders, risk teams and operations leaders, understanding agentic AI is essential for planning how work will change.

Definition of Agentic AI

Agentic AI is a class of AI systems that exhibit agency, meaning they interpret goals, plan and execute sequences of actions, use external tools and information, evaluate outcomes and adapt with limited human direction, either as individual agents or as coordinated groups, within boundaries set by people.

It is delivered through AI agents, agentic workflows and multi-agent systems, and requires agentic AI governance.

Agentic Design Patterns

Planning. The system breaks a goal into steps and decides the order in which to perform them.

Tool use. The system calls external functions, such as search, databases, APIs or other software, to gather information or take actions.

Reflection. The system reviews its own outputs, checks for errors and revises its work.

Memory. The system keeps track of what it has done and what it has learned during a task or across tasks.

Collaboration. Several agents with different roles work together, or the system works alongside people.

Generative AI vs. Agentic AI

Interaction. Generative AI typically produces content in response to a prompt, while agentic AI pursues a goal over multiple steps.

Action. Generative AI outputs text, images or code, while agentic AI also takes actions in other systems.

Autonomy. Generative AI relies on a person to decide what to do with its output, while agentic AI makes some of those decisions itself.

Risk. Because agentic systems act, the consequences of errors are greater, requiring stronger controls.

Importance of Agentic AI in Enterprise Operations

It automates complex work. Tasks that involve judgment, several systems and many steps can be partly or fully handled by AI. This goes beyond earlier automation, which handled only predictable, rule-based tasks.

It changes the economics of work. Organizations can scale certain activities without proportional increases in staff. People can spend more time on judgment, relationships and creative work.

It also raises the stakes for governance. Systems that act need clear permissions, oversight and accountability. The governance practices used for generative AI are not enough on their own.

Agentic AI and Financial Management

Agentic systems often use many model calls per task, so costs can be higher and less predictable than for simple generative AI.

Measuring AI productivity and outcomes is necessary to judge whether agentic systems are worth their costs.

Investment in platforms, data, security and monitoring is required alongside model costs.

Business cases should account for the human oversight still needed, especially early in deployment. Oversight costs usually fall as agents prove reliable and autonomy increases.

Common Challenges

Reliability is uneven, and agents can fail in unexpected ways on complex or unfamiliar tasks. Small errors early in a long task can compound into larger problems later.

Security risks grow as agents gain access to more systems and data. Each tool an agent can use is a potential path for misuse if the agent is manipulated.

Accountability is unclear when an agent's actions cause a problem. Organizations need to decide in advance who owns each agent and who answers for its decisions.

Hype leads organizations to apply agentic AI where simpler automation would work better. A fixed workflow or rule-based automation is often cheaper and more reliable for predictable tasks.