Agentic AI is: One of the most talked-about subjects in relation to artificial intelligence – and for good reason. It represents a shift from AI systems that simply respond to prompts, into systems that can act, taking initiatives and carrying out multi-step tasks.
AI agents can plan actions, use tools, and adapt based on feedback, making them far more useful in real-world business workflows. This shift marks a transition from passive AI assistants to active AI agents that focus on outcomes rather than targeted responses.
So, what exactly is Agentic AI, how do these workflows operate, and what can agents realistically do? Let’s break it down.
What is Agentic AI and how does it operate?
Agentic AI refers to AI systems designed to behave like “agents”—meaning they don’t just generate text, they can take actions towards reaching a goal. Often these actions include multiple steps such as research, data analysis, content generation or tool usage. An agent achieves results by determining the steps and executing them.
Agentic Workflow Operations – Think, Act, Observe
Below is an example of how an agent might work in researching a market:
- Search for sources
- Read and extract key information
- Compare and rank findings
- Write a summary
- Notice missing info
- Search again
- Produce a final report
Agentic AI workflows operate through an iterative loop often described as think, act, and observe. First, the AI agent interprets the goal and plans the next best step. It then performs an action, such as calling a tool or retrieving data, and observes the result before deciding what to do next.
It is important to notice the “observation” aspect in this workflow – the steps Notice missing info and Search again. Agentic workflows are designed to incorporate feedback within the loop. Agents can validate results against an end goal to determine if the results need refining. Including humans in the loop is an important step in providing feedback to verify if results are on target. Each level of verification improves the confidence in which an agent operates, leading to better results and less dependence on humans.
In relation to the “action” step in the loop, agents need access and permissions to the right tools and data, to get the job done. Access and permissions are defined when building the agent.
The complexity of tasks and the type of work deployed can greatly impact overall function, success and work put into making an agent and Agentic Ai work the way you expect it to. The key factors to how agents work together are in a way similar to how we as people work together by building relationships and tasks that build off of one another that the agents can share to produce the final desired completed task.
SphereGen offers a range of solutions that utilize Agentic AI and Automation systems. If you are interested in learning more or have questions about how Agentic AI can assist in your workflows. Contact us today and we can explore various solutions together.