An Introduction to Agentive AI: What It Is and How It Is Used

AI gained mass mindset through the advent of ChatGPT and generative AI, but that is only one form of AI available. Agentive AI is another potent form of AI that people are just now beginning to understand.

Agentive AI, or AI agents, provides a new way for systems to autonomously carry out tasks, make decisions and interact with their environments. It's unlike traditional AI, which often requires human input or intervention. Instead, Agentive AI operates independently and autonomously to handle complex operations, offering businesses and users the ability to automate processes and solve problems without human input or oversight.

At its core, Agentive AI is powered by advanced machine learning (ML) algorithms, natural language processing (NLP), decision-making frameworks and robotics. Agentive AI is far more adaptive than generative AI. It can take independent action, learn from new data, adapt to changing circumstances and improve over time.

Key Characteristics of Agentive AI
Agentive AI has several defining characteristics.

• Autonomous information processing to derive insights and inform the app's actions.
• Make real-time decisions and take actions such as making a recommendation or adjusting operations in a supply chain.
• Adapt to new situations by learning from new data and past actions, improving their performance over time and adapting to unforeseen conditions or tasks.
• Unlike generative AI, which specializes in providing insights or recommendations, Agentive AI takes action to fulfill its goals or tasks.
• Agentive AI systems are often designed to interact with humans, devices, or other AI systems to perform their duties effectively.

How Agentive AI Is Changing Business
One of the major aspects of AI in general and agentive AI in particular is automation. Computers don't get tired and they don't make mistakes unless they are misprogrammed. Agentive AI can also help improve decision making by providing analysis of data, which allows the business to make a more informed, data-driven decision.
The result is improved efficiency and performance across the business and freeing up humans to do other tasks that AI cannot do. By minimizing the need for human intervention in routine tasks, companies can significantly reduce labor costs and improve their bottom line.

What Are Some Use Cases for Agentive AI?
The versatility of Agentive AI makes it applicable across a wide range of industries. Below are some of the most common use cases where Agentive AI is having an impact:

Customer Service and Support: ChatBots are already a popular and high profile example of AI. Through AI agents, chatbots can autonomously interact with customers, respond to inquiries, and solve issues in real-time, rather than passing off the work to human. More advanced agent systems can even detect the customer's mood, understand complex queries, and offer personalized responses.

Personalized Sales and Marketing: Sales and marketing teams benefit from Agentive AI systems much in the same way as customer service and support. It can offer personalized communications with customers, recommend products or services, and predict customer behavior. A common example of this would be Amazon's recommendation engine, which suggests products based on past purchases and browsing patterns.

Supply Chain Optimization: Agentive AI can have an impact on business operations like logistics, inventory management and production scheduling. These AI systems autonomously monitor inventory levels, predict demand fluctuations, and even reorder stock automatically based on predefined parameters. They can optimize routes for deliveries, reduce delays and make supply chains more agile and cost-effective.

Healthcare: Agentive AI in healthcare can be used to manage patient data, recommend treatment options and even assist with medical diagnoses. AI-powered agents can monitor patient vitals in real-time, alert healthcare providers to potential health risks, and suggest the most effective courses of treatment based on medical records and current health conditions.

Financial Services: Financial institutions use Agentive AI for a variety of applications, from fraud detection to market trends analysis to predicting stock prices, and make real-time decisions on what actions to take. They can also identify unusual activity in bank accounts or credit card transactions, flagging potential fraud without human intervention.

Package Software vs. Homegrown
AI agents can be both available as packaged software and custom-built, depending on the specific needs and use cases. Packaged AI software is available from a number of ISVs and by and large are ready to use for general tasks. These are pre-built solutions that are ready to use without needing extensive customization.

Custom AI agents are built for specific, often complex use cases and are highly customizable. Building custom AI agents allows full control over their functionality, data sources and training, but you may not have the expertise in house development. That's where a consulting service such as Accenture or Deloitte comes in.

Packaged software includes:

• Chatbots (like Intercom, Drift or Zendesk) for customer support.
• Virtual assistants (like Apple's Siri, Amazon's Alexa, Google Assistant)  for general-purpose use.
• Analytics tools (like IBM Watson Analytics, Tableau and Microsoft Power BI) for business intelligence and data analysis.
• Automated marketing tools (like HubSpot, Mailchimp or Marketo) for managing campaigns with AI-driven insights and recommendations.
• Customer Relationship Management (CRM) (like Salesforce Einstein, PandaDoc and Zoho CRM) for customer relationship management, lead scoring, sales predictions and customer interaction tracking.

These solutions usually offer APIs and integration options, so they can be customized to some extent.

For more specialized or advanced applications, AI agents are often custom-built in house. In this case, you would have to partner your data scientists with your engineering team to create an AI agent tailored to your specific needs.

That's our introduction day generative AI. In our next blog, we will explore Microsoft's specific efforts around agentive AI.

Posted by Andy Patrizio on 04/08/2025