What Is Agentic AI?

Agentic Artificial Intelligence (AI) refers to autonomous AI systems capable of independently executing multi-step tasks, making decisions, and taking actions to achieve specific goals without constant human supervision.

Unlike traditional AI that responds to individual commands, agentic AI operates more like an independent agent that plans, reasons, and adapts its approach based on changing circumstances.

These advanced systems represent a significant evolution in AI technology, combining the knowledge capabilities of large language models with the action-oriented functionality of automation tools. This allows them to handle sophisticated workflows, interact naturally with users, and optimize processes in real-time.

Agentic AI vs Traditional AI

Agentic AI is the newest iteration of traditional AI technology, which has been developing for decades. They are both built on machine learning algorithms, but they have different capabilities. The similarities and differences between the two can be understood as follows:

Traditional AI

Designed for specific, narrow tasks like pattern recognition, data classification, or predictive analytics.
Requires explicit instructions and operates within rigid parameters.
Cannot reliably evaluate multiple options or execute multi-step workflows.
Examples include: recommendation engines, image recognition, or voice assistants.

Agentic AI

Features autonomous decision-making and goal-oriented behavior.
Proactively takes actions to accomplish goals and adapts to new situations
Interacts with multiple systems or environments without requiring constant human oversight.
Manages complex task sequences and learns from outcomes to improve performance.
Examples include: customer service agents, intelligent process automation.

The fundamental difference: traditional AI is reactive and task-specific, whereas agentic AI is proactive and goal-driven.

How Does Agentic AI Work?

Agentic AI functions as an intelligent mesh between generative AI (gen AI), which is built on large language models (LLMs), and traditional automation systems.

Like traditional AI, agentic systems are designed to complete tasks, but they have the same ‘knowledge base’ as generative systems. This means that they are an intelligent automation solution, using generative outputs alongside external tools to execute complicated actions.

To develop and act on information, agentic AI systems use several methodologies and tools:

Goal Setting and Planning: Agentic AI can define objectives based on input or pre-set targets and devise strategies to achieve them.
Autonomous Decision-Making: Using advanced machine learning models, it evaluates available options, predicts outcomes, and selects optimal actions.
Contextual Understanding: Through natural language processing (NLP) and environmental sensors, agentic AI interprets the context in which it operates, enabling more accurate decision-making.
Continuous Learning: Agentic AI utilizes feedback loops to evaluate results, learn from successes and failures, and dynamically adjust strategies.
Multi-Agent Collaboration: In some cases, multiple agentic AIs coordinate with one another, sharing information, and dividing tasks to optimize outcomes.

In a business context, agentic AI represents a significant advancement over generative AI and traditional AI. Consider a customer support scenario: while gen AI might diagnose a customer’s problem and suggest solutions, agentic AI takes the next step by directly implementing the fix, updating relevant systems, and following up to ensure resolution.

Applications of Agentic AI

Agentic AI can be a useful copilot in every task that requires evaluation of options to meet an objective, which means that the eventual applications of agentic technology are vast.

Since these systems are still in a relatively early stage of development, they are yet to reach their full potential of fully unmonitored use for any purpose. That being said, they can still be helpful for clients looking for support in certain areas, especially in customer service and with advanced automation.

Customer Service and Experience

Agentic AI creates intelligent virtual assistants and chatbots that move beyond simple query responses to proactive customer engagement that:

Anticipates customer needs based on interaction history and behavioral patterns.
Troubleshoots problems independently and implements solutions.
Personalizes interactions based on customer preferences and context.
Escalates issues to human agents when necessary.
Continuously learns to improve service quality and response times.
Assists human agents with context while they answer support calls.

Leading businesses have developed enterprise-ready customer experience solutions already. They offer truly round-the-clock digital architecture where questions can be answered in seconds. This saves support staff time and delights customers, who feel heard and have their issues resolved almost instantaneously.

Intelligent Automation

Alongside customer service, the key use case of agentic AI systems currently is automating tasks that were previously too complex for automation. Key examples include:

FRAML (Fraud, Risk, and Anti-Money Laundering): An AI-led platform developed to screen payments and customers, monitor transactions, and detect fraud, where agentic AI supports other automated systems.
Agentic AI in Insurance: End-to-end support of underwriting through analysis, personalization, and monitoring.

Tasks of this type are often tedious and complicated, leading to errors when completed by human agents; when automated, results become quick and reliable. This level of automation also increases business agility, as AI solutions can be altered quickly, enabling companies to respond quickly to market changes.

Sutherland Can Help You Automate Your Business

Sutherland has positioned itself at the forefront of the agentic AI movement, developing solutions that allow businesses to unlock the true potential of these systems. We have 38+ years of experience in facilitating transformative change for organizations across a range of sectors, so we understand your industry-specific challenges and avenues for growth.

We have developed end-to-end agentic AI customer experience (CX) support solutions, assisting you by automating support conversations, providing context to human agents on call, and analyzing your interactions to deliver precise feedback.

Our agentic AI automation solutions can expedite intricate tasks, boosting the productivity of your agents. So far we have developed extensive systems that ensure data security and information accessibility, as well as supporting analysis for underwriting or FRAML purposes.If you’re interested in exploring how agentic AI can give your business a competitive edge, feel free to get in touch.

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