What is Agentic AI?

Agentic Artificial Intelligence (AI) is characterized by its ability to solve complex, multi-step problems autonomously using real-time data.

How does Agentic AI work?

Agentic AI integrates with large language models (LLMs) to make decisions, leveraging advanced AI models for sophisticated reasoning and iterative planning. Agentic AI deploys autonomous agents to interact with enterprise data, process workflows, perform and automate tasks, and work together without human intervention.

Agentic AI vs. Generative AI

The key difference between Agentic AI and generative AI (genAI) lies in their core capabilities, goals, and behavior.

GenAI focuses on creating new content based on existing data inputs, focusing on creativity. GenAI is used for content generation, image creation, and natural language processing (NLP).

Agentic AI is an autonomous problem solver designed to perform autonomously by analyzing data and setting goals. Agentic AI focuses on real-time decision-making and executing complex workflows.

GenAI and agentic AI can still work together well despite their key differences. For example, a virtual AI assistant can utilize agentic AI to handle real-time customer interactions, such as answering inquiries, guiding the customer conversation, and making decisions based on user input. GenAI can create and deploy nuanced responses for specific questions tailored to steer the conversation accordingly.

Agentic AI vs. non-Agentic AI

The main difference between agentic and non-agentic AI is how they respond and interact with their environment.

Proactive: Agentic AI acts as agents who can autonomously perceive, reason, plan, and act to achieve their specified goals in an evolving landscape.
Reactive: Non-agentic AI does not act autonomously. Instead, it processes input and responds accordingly if prompted. Non-agentic AI does not act or make decisions unless asked or preprogrammed.

Benefits of using Agentic AI

By leveraging and deploying agentic AI in their business objectives, enterprises can enhance productivity, improve decision-making, and increase agility and adaptability.

Agentic AI use cases span across industries, from customer service to healthcare and manufacturing:

Customer service - Intelligent chatbots and virtual AI agents can automate customer support tasks such as answering customer inquiries and processing requests. AI agents can autonomously provide real-time problem resolution and personalize customer interactions.
Healthcare and administrative - AI agents can automate routine administrative healthcare tasks to save human medical staff time and resources. AI agents can deploy real-time patient data analysis to assist doctors in improving healthcare delivery.
Manufacturing - AI agents can optimize production workflows through autonomous monitoring systems, boosting productivity and cutting downtime costs.
Supply chain - AI agents can enhance supply chain management with predictive analytics and automation.
Sales & marketing - Sales teams can leverage AI sales reps to respond immediately to inquiries, using preprogrammed language. Sales teams customize how often, which channels, and when AI agents engage with customers and prospects before transitioning or escalating requests to human employees. AI agents can optimize and accelerate your marketing campaign efforts by creating campaign briefs tailored to target audiences and relevant content. AI agents can also continuously analyze campaign performances against KPIs and proactively recommend improvements.

AI agents in action

Autonomous driving agents (self-driving cars) - These AI agents use cameras and radar detection technology to observe their surroundings and make driving decisions. They use their environment to autonomously plan routes, detect road obstacles and traffic conditions, and follow traffic laws. They can also steer, accelerate, brake, and change lanes autonomously.
Task execution agents (warehouse robots) - Fulfillment companies like Amazon use these AI agents to work in warehouse environments. They deploy sensors to locate items, navigate warehouse layouts, and move items around without human intervention. Warehouse AI agents can autonomously decide on optimal routes to pick up and deliver goods, ultimately optimizing order fulfillment efficiency.
Healthcare diagnostic agents - These medical assistance agents can read medical images, decipher test results, and memorize patient histories. They leverage medical data to make diagnoses or flag abnormalities. These agents can also provide patient recommendations and treatment with a doctor’s approval. OneRemission is a health-focused AI chatbot that supports cancer patients, survivors, and caregivers. Initially developed by wellness experts and oncologists, OneRemission deploys a holistic approach to help users manage cancer-related symptoms and provide post-treatment recovery.
Scheduling assistants - AI agents like x.AI and Reclaim.ai are virtual assistants that help businesses manage and maintain calendar availability, deadlines, and meetings. These autonomous agents can decide when and how to schedule and reschedule events, book meetings, and send invites.
Autonomous drones (Multi-Agent Systems) - Autonomous AI-powered drones can fly, capture images, and drop payloads as needed. These AI drones are often used for military and security surveillance, agriculture, delivery, and disaster response. The drones can perceive weather conditions and navigate terrain, identifying obstacles, flight paths, targets, and more.
Banking and finance virtual assistants - Erica by Bank of America is a virtual financial assistant that uses NLP and real-time data to respond to customer inquiries. This AI agent helps manage bank transactions, pay bills and invoices, track spending costs, and offer financial advice.

The future of Agentic AI

As businesses continue to navigate artificial intelligence technology, agentic AI offers a transformative approach to problem-solving and deploying operational efficiency. As the digital landscape evolves, so will the capabilities and deployment of agentic AI.

Emerging agentic AI trends to look out for:

The integration of synthetic data with real-world data creates optimized learning models.
The development of advanced multi-agent systems for complex problem solving.
The rise of role-based agents tailored to unique and specific enterprise needs.

By leveraging agentic AI capabilities, enterprises can future-proof their business longevity by harnessing new avenues for innovation and growth.

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