Agentic AI Flows

Updated on 17 Jan 2025
3 Minutes to read

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Overview

Agentic AI Flow is a self-directed, intelligent workflow powered by AI agents that operate independently, executing tasks, learning from outcomes, and optimizing workflows to drive smarter results.

Role of Agentic AI Flow component in AIW

In the context of AI for Work (AIW), an Agentic AI Workflow enables action-oriented AI that goes beyond data analysis by making decisions and automatically taking actions to accomplish tasks or achieve specific goals.

Let's understand it's role with the following analogy:

Analogy - Role of Agentic AI Flow

Consider an IT Service Desk where an employee submits a support ticket due to their computer's slow performance.

Context: The AI knows it’s a performance issue and automatically checks if the employee’s machine needs software updates or if there’s a memory issue.

Decision-Making: The AI decides that the issue might be due to outdated software and automatically triggers an update, or it could suggest the employee restart the computer.

Executing Actions: The AI either fixes the issue itself (by pushing an update or clearing cache) or escalates the issue to the IT technician if the problem is more complex.

Learning and Adapting: In the future, if similar slow-down issues are reported, the AI will prioritize the software update step based on past success.

Best Practices

Consider the following best practices for implementing Agentic AI Flows to achieve more effective results.

Best Practices to implement Agentic AI Flow

Ensure High Quality Data
Agentic AI Flows depend on high-quality data for effective decision-making and continuous learning. The better the data quality, the more accurately the AI can interpret context, make decisions, and execute actions.
Define Clear Objectives
Before implementing Agentic AI Flow, it’s crucial to clearly define the specific business goals and use cases it will address.
Identify key pain points and operational bottlenecks that can benefit from AI automation
Continuous Learning and Adaption
One of the key strengths of Agentic AI Flows is their ability to learn from past actions and outcomes. To maximize this capability, the AI system must be continuously trained and updated with new data, user feedback, and evolving business needs.

Use Case

Use-Case

Solution

NovaTech IT solutions, an employee submits an IT support ticket because their laptop is running slowly. This is a common issue reported frequently by employees, leading to a backlog of tickets in the IT service desk. Traditionally, IT technicians manually troubleshoot the problem, which can take time. They typically go through a set of troubleshooting steps—checking for updates, cleaning temporary files, and verifying system settings—before identifying and resolving the issue.

With high ticket volume and repetitive tasks, this process is time-consuming, and IT staff are overwhelmed by the sheer number of similar requests.

To address this requirement, the Agentic AI Flow System automates the incident resolution process, improving efficiency and reducing the burden on the IT team.

Ticket Analysis

Once the employee submits the ticket, the Agentic AI Flow automatically scans the ticket for relevant details such as the type of issue (slow performance), employee information, and the device involved. The AI identifies that this issue is a typical performance problem, based on previous tickets and logs.

Decision Making

The AI flow evaluates common causes of slow performance, such as outdated software, excess background processes, or insufficient disk space. It decides on a course of action to resolve the issue quickly. Over time, the AI adapts and fine-tunes its decision-making process, leading to faster resolution for future tickets.

Automated Actions

The AI initiates predefined actions to resolve the issue:

Clearing temporary files to free up space.
Checking for software updates and automatically applying any available updates.
Optimizing startup programs to improve system performance

Ticket Resolution

Once the actions are completed, the AI automatically updates the ticket with the resolution remarks.

Configured Notification Templated can be used in various languages, this localization helps to achieve the required customer satisfaction.

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Overview