What are Agentic AI Workflows?

1. Understand goals and context

An agentic AI workflow is designed with clear goals and parameters to guide its operation. 

In this case, the goal is to efficiently schedule patient appointments by considering factors such as doctor availability, patient preferences, and clinic resources.

The AI agent is programmed to understand the specific rules and context it operates within, including clinic hours, doctor specializations, and patient history.

2. Analyze live data

The AI agent continuously analyzes live data, including updates on which doctors have called in sick, or which appointments have been rescheduled.

For example, if a doctor becomes available due to a cancellation, the AI agent processes this information to identify patients who could benefit from the newly opened slot.

3. Make autonomous decisions

Based on the analyzed data, the AI agent evaluates options and autonomously decides the best course of action. 

For instance, if a patient cancels on the same day as their appointment, the AI agent will autonomously weigh which patients are most likely to take a last-minute appointment. It may start by calling patients with urgent health matters until it finds someone willing and able to take the appointment.

4. Execute tasks proactively 

The AI agent then notifies the most suitable patients, updates the information in the doctor's schedule, and confirms the appointment - all without the need of human input.

Sales

From simply deploying a sales chatbot as a customer engagement tool to implementing fully automated sales processes, agentic AI workflows are transforming how businesses engage with customers.

AI chatbots can provide instant responses to customer questions, recommend products based on browsing history, and guide users through the checkout process. Reminders about abandoned carts or time-sensitive offers can ensure that no sales opportunity is missed.

Marketing

Agentic AI workflows, such as chatbot marketing, transform marketing by delivering personalized promotions, analyzing live data, and optimizing outreach. 

Chatbots engage customers with tailored interactions, recommend products, and send reminders for abandoned carts. 

During peak periods like holiday sales, they dynamically adjust promotions to maximize revenue through:

• Product recommendations
• Personalized discounts
• Real-time promotion adjustments

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Healthcare

Agentic AI workflows facilitate scheduling patient appointments by optimizing doctor availability and improving patient satisfaction.

These workflows proactively send reminders, reschedule appointments when necessary, and prioritize patients based on urgency and specific healthcare needs. 

Supply chain management

Real-time demand forecasting and dynamic inventory management are two key ways agentic AI workflows are transforming supply chain management. These workflows leverage live data to minimize delays, reduce costs, and optimize efficiency at every stage of the supply chain.

For example, an AI agent might predict spikes in demand and automatically adjust inventory levels to meet these changes. During events like Black Friday, when demand is typically higher, AI agents can anticipate this and proactively increase inventory levels.

Conversational AI workflows

Conversational AI workflows, such as those used in AI-powered assistants, involve guiding users through multi-step interactions based on context, intent, and historical data. 

These workflows are designed to provide a personalized and efficient experience by anticipating user needs and autonomously adapting responses. 

For example, an AI-powered customer support workflow can handle routine queries, such as order tracking or account troubleshooting, by guiding users through a series of context-aware questions. 

It can escalate more complex issues to human agents while providing detailed conversation history. High chatbot containment rates, measuring the percentage of interactions resolved without human intervention, highlight the success of these workflows in reducing costs and improving response times.

Multi-agent systems (MAS)

In multi-agent systems (MAS), multiple AI agents operate collaboratively within the same environment to solve complex, distributed problems. 

MAS leverage agentic AI workflows to enable agents to exchange data, coordinate actions, and make joint decisions in real time, facilitating task breakdown and the achievement of shared objectives.

For example, a retail business could implement a MAS to manage an autonomous delivery fleet, where multiple delivery drones or self-driving vehicles collaborate to optimize delivery times and routes. 

These systems allow the vehicles to communicate and adapt to real-time conditions, such as road closures or weather changes, ensuring efficient and timely deliveries.

Reinforcement learning (RL) agents

Reinforcement learning (RL) agents learn through trial and error, adjusting their decisions based on the rewards or penalties received from their actions. 

For instance, in warehouse robotics, an RL agent might optimize the movements of a robotic arm to pick and pack items more efficiently. Initially, the agent may make mistakes, such as misjudging the placement of an item, but over time, it learns the most efficient paths and actions by maximizing the reward signal for successful tasks.