What is agentic AI?
Agentic AI is the class of AI systems that pursue a goal with some independence. They plan steps, use tools and software to take actions, check the results and adjust, while people set the goal and the limits.
What is the difference between agentic AI and generative AI?
Generative AI produces content when asked: a paragraph, an image, a block of code. Agentic AI uses that same generative capability to get something done. It decides on steps, acts in other systems and checks whether the goal is met.
| Generative AI | Agentic AI | |
|---|---|---|
| Input | A prompt | A goal |
| Output | Content for a person to use | A completed task, plus a report |
| Interaction | One request, one response | A loop of plan, act, observe |
| Reaches other systems | Rarely | Through tools and APIs |
| Main risk | A wrong answer | A wrong action |
The two work in layers: almost every agentic system has a generative model at its core. What makes it agentic is the loop and the tools around that model.
What makes a system agentic?
Most definitions share four traits:
- Goal-directed. It works toward an outcome across several steps.
- Plans. It breaks the goal into steps and revises the plan when a step fails.
- Acts. It calls tools, APIs or other software to change something outside itself.
- Uses feedback. It reads the result of each action and decides what to do next.
Autonomy is a dial, not a switch. At the low end an agent proposes each action and a person approves it. At the high end it runs a long task unattended and reports back. Most business uses sit near the middle: the agent acts on routine steps and asks before anything costly or irreversible.
Agentic AI is the broad paradigm. An AI agent is one system built on it, and the multi-step jobs that agent plans and runs are its workflows.
What does agentic AI look like inside SaaS products?
In business software, agentic AI changes who does the clicking. Today a user who wants a result learns the menus, finds the settings and does each step. With an agent in the product, the user states the result and the agent does the steps.
Picture an HR platform where a manager types: "Start onboarding for the three engineers joining Monday." The agent creates the employee records, assigns the standard engineering checklist, books the orientation session and asks one question it can't answer itself (which laptop model each person needs).
For this to work, the agent needs the product's actions exposed as tools, the signed-in user's permissions applied to every call, and a team that can see what users ask for and where the agent falls short.
That is the model Foldspace is built for: the user names the result, then the Product Agent does the clicking for them in the product, bounded by that user's existing permissions.